{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shuwen-Fang/Inference-Quickstart/blob/main/%5BEXTERNAL%5D%20Inference%20Quickstart%20Colab%20Visualizations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wf5KrEb6vrkR"
      },
      "source": [
        "# Explore Benchmarking Data with Inference Quickstart\n",
        "This notebook is a hands-on guide to the Google Inference Quickstart (GIQ) recommender API. It's designed to help you explore performance and pricing metrics for serving open-source Large Language Models (LLMs) on Google Kubernetes Engine (GKE).\n",
        "\n",
        "By the end of this guide, you'll be able to compare the price and performance of serving LLMs with different accelerators on GKE, understand the critical trade-offs between latency, throughput, and cost, and select the optimal hardware configuration to meet your specific performance goals and budget."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdRyKR44dcNI"
      },
      "source": [
        "\n",
        "## Authenticate and Initialize Environment\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "JBQHM2H7OHrJ",
        "outputId": "f5e35443-f5be-4334-bb52-4d03847984a5"
      },
      "outputs": [],
      "source": [
        "project_id = \"\" # @param {type:\"string\"}\n",
        "\n",
        "if not project_id:\n",
        "  raise ValueError(\"Please enter your Project ID in the 'project_id' variable.\")\n",
        "\n",
        "# Import all libraries\n",
        "from IPython.display import Image, display\n",
        "import uuid\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as mticker\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# Initialize shared functions\n",
        "def convert_to_decimal_cost(cost_dict):\n",
        "  \"\"\"Converts cost from a dictionary with 'units' and 'nanos' to a decimal representation.\"\"\"\n",
        "  units = int(cost_dict.get(\"units\", 0))\n",
        "  nanos = int(cost_dict.get(\"nanos\", 0))\n",
        "  return units + nanos / 1e9\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XHys5objvFy0"
      },
      "outputs": [],
      "source": [
        "# Use the outputted link to authenticate your gcloud access\n",
        "!gcloud auth login\n",
        "!gcloud config set project {project_id}\n",
        "!gcloud services enable gkerecommender.googleapis.com --project {project_id}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7109cod-TJhV"
      },
      "source": [
        "## Explore supported configurations\n",
        "Before diving into a specific model, let's get a high-level view of the model supported by GIQ and how they compare in terms of intelligence and cost."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o40lf7g0Rp3t",
        "outputId": "7c4015bc-3b8c-4326-f312-23dbeea4ce70"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Supported Models:\n",
            "- Qwen/Qwen3-32B\n",
            "- deepseek-ai/DeepSeek-R1\n",
            "- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B\n",
            "- google/gemma-2-27b-it\n",
            "- google/gemma-2-2b-it\n",
            "- google/gemma-3-27b-it\n",
            "- google/gemma-3-4b-it\n",
            "- meta-llama/Llama-3.2-1B-Instruct\n",
            "- meta-llama/Llama-3.3-70B-Instruct\n",
            "- meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\n",
            "- meta-llama/Llama-4-Scout-17B-16E-Instruct\n",
            "- meta-llama/Meta-Llama-3-8B\n",
            "- mistralai/Mistral-Small-24B-Instruct-2501\n",
            "- mistralai/Mixtral-8x22B-Instruct-v0.1\n",
            "- mistralai/Mixtral-8x7B-Instruct-v0.1\n",
            "- openai/gpt-oss-120b\n",
            "- openai/gpt-oss-20b\n"
          ]
        }
      ],
      "source": [
        "supported_models_json = !curl \"https://gkerecommender.googleapis.com/v1/models:fetch\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\"\\\n",
        "  -A \"google-colab\"\n",
        "\n",
        "supported_models_str = \"\".join(supported_models_json)\n",
        "supported_models_data = json.loads(supported_models_str)\n",
        "print(\"Supported Models:\")\n",
        "for model in supported_models_data[\"models\"]:\n",
        "  print(f\"- {model}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TE0G2WIYzhqk"
      },
      "source": [
        "### Compare Model Intelligence and Cost\n",
        "Model elo ratings are retrieved from the Chatbot Arena as of July 2025. This analysis focuses on models supported by both Google Inference Quickstart (GIQ) and Chatbot Arena for a comparative view of intelligence and cost."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "PdSpsCx40Bph",
        "outputId": "88580929-a27f-4bdb-cb24-09c89197f82a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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v7m5XFeTfR5s2bTBr1qwKvVbNmjXRpEkTBAUFFUgk5S9309PTg4uLS6Hnd+rUCU5OTjh+/DguXryIGzduIC4uDm/fvoWvry98fX3Rq1cv/PTTTxVeJ8nAwKDEovPFqYw6TkRERPRpYyKJiIiqpfwlcikpKbCxsfmgOjAVQUdHR3gcFxcHKyurIvsWN1sn/z5TU1M/KKHwofT19YXH0dHRZT5fT08PUVFRyMrK+ij30b17dwQFBeH58+e4e/cu7OzskJycjPPnzwPIq6eTX8C5MNra2ujXrx/69esHAAgJCYG/vz927dqFqKgoHD58GF988UWhRdQ/Jfr6+oiMjCzX1/xjeP/nrDjlnd1HREREsvgxFRERVSmlrUGTXxcpMzNTqL9TldSpU0d4fP/+/WL7Fjf+d4tfV+Yf8xYWFsIf7devXy/z+fn3ce/ePWRmZsp1bIXp0qULlJTyPi/Ln4V08uRJZGRkACh8WVtxbGxsMHbsWBw8eBAaGhoAIOwa+Cl79+v2sZaFlkXdunWFxx/yc0ZERESlx0QSERFVKe/uulRcwqFdu3ZC0mnHjh0VPq6ysrOzE+rY+Pr6FtkvJiYGFy9eLPK4m5sbAEAqlZZpCZ+8KSgowNXVFUBeIunBgwdlOj//PpKSkuDj4yP38b1PLBajdevWAIBjx44hNzdXSCiZm5vDwcGhXHHNzMyEHcTerRv1qcr/uqWlpeHAgQOVPJqCTE1Nha+Hn59fkf9nZGRkwM/P7yOOjIiI6NPFRBIREVUpxsbGwuMXL14U2c/a2lrYzvvYsWPYvn17sXFfvnxZ5HbwFUFVVRU9e/YEANy9e7fQZFdubi7mz58vzJIpjLOzM+zt7QEA3t7eOH78eLHXDQ4OxtmzZ8s/8GKMGjUKCgoKkEqlmD59OiIjI4vs+/6xXr16wczMDADw888/lzir6caNG8IW9+WVP+soOjoaR48eRWBgIACgW7duRc58O3PmjEwB5/dFREQI280XVm9ryJAhkEgkkEgkePXq1QeNvyr46quvhHpba9euLfZrUtz3Q0Xq37+/cP2VK1cW2mfFihWIior6mMMiIiL6ZLFGEhERVSlNmjQRHi9btgzjx4+HkZGR8Ie/ubm5sGRp4cKFuHfvHl6+fInly5fD398fPXr0QN26daGiooL4+Hg8evQIFy5cwNWrV9GxY0d069bto93L5MmT4efnh+joaPz000+4f/8+unfvDrFYjLCwMOzcuRNBQUGwt7fHnTt3ABS+tG/VqlXo06cP4uPjMX36dPj6+sLd3R21a9eGgoICYmNj8fDhQ5w7dw63b9/GyJEjhZkk8vTFF19gypQpWLduHZ4/f47u3btj4MCBcHR0hJ6eHhITE/Hw4UOcPn0aCgoK2LVrl3CuiooK1q5diyFDhiA1NRXDhg2Du7s7OnToAAsLC+Tm5iI6Ohr379/H6dOn8fjxY8ybNw8tWrQo93jbt28PDQ0NpKamYunSpULB7+KWte3YsQMzZ86Eq6srHB0dYWNjA21tbSQkJODevXvYvXu3sJvcgAEDyj226kJVVRUrVqzAqFGjkJaWhhEjRuCrr75Chw4dYGpqiszMTDx79gwBAQE4e/ZspSwfGzRoEHx8fPD48WPs2LEDL168QJ8+fWBqaorIyEgcPHgQ58+fL/HnjIiIiEqHiSQiIqpSLC0t0aVLF5w4cQIXL14ssOzL399fmAmip6eHffv2Ydq0abhx4wauX79e7EwXTU3NCh37+/T09PDbb79hxIgRiIuLw5EjR3DkyBGZPr1794aDg4PwB66qqmqBOLVq1cL+/fsxdepUPH78WGZL+sJU5H1OnDgRCgoK8PT0RGJiIjZt2oRNmzYV6FdYAqhx48bYtWsXpk2bhoiICBw9ehRHjx4t8lpaWlofNFYNDQ20b98eR48eFWYZ1a9fX6auTmHS0tLg5+dX5FIoBQUFTJkyBR06dPig8VUXjo6O2LRpE7777jskJCTAx8fnoyxPLC0VFRVs3rwZw4YNw4sXLwr9+XB2dsawYcMwZswYAIX/nBEREVHpMJFERERVzi+//AJbW1ucPHkSoaGhSElJQW5ubqF9jYyMsGfPHpw/fx5///03bt++jZiYGGRnZ0NbWxuWlpZo0qQJ3Nzc0Lx58498J3mJi2PHjmHr1q04e/YsXr9+DU1NTdSrVw99+/ZFt27d8Pvvvwv98+sqvc/Kygp//fUXTpw4gVOnTuHu3buIi4tDTk4O9PT0YGVlBQcHB3Ts2BENGzas0HsaP348OnfujL179+LKlSt4/fo10tPToaOjgzp16sDJyUlY1ve+xo0b49SpU/Dx8cG5c+fw4MEDvH37FgoKChCLxbCxsUHz5s3x5Zdfwtra+oPH2r17d5lkVUlFtletWoXz588jMDAQISEhiImJwdu3b6GiogJzc3M0a9YM/fv3R/369T94bNWJi4sLzpw5g3379uHcuXMIDQ1FUlIS1NXVYWlpiWbNmn3U2X7vq1GjBo4cOYJt27bh5MmTePHiBVRUVGBtbY0ePXqgf//+Mks+PzRJSURE9DkTSaVSaWUPgoiI6HP2v//9D3/88QdMTU0REBBQ2cMh+iT9+uuvWLduHZSUlHDr1i3OSiIiIionFtsmIiKqROnp6fD39wcANGrUqJJHQ/RpkkqlOHHiBIC8WYJMIhEREZUfE0lEREQV6MWLFyhq8m9OTg4WLlwobCPfq1evjzk0ok/Gq1evkJ2dXeTxdevW4fHjxwD4c0ZERPShWCOJiIioAv3666+4c+cO3N3d0ahRIxgYGCA9PR3BwcE4dOgQ7t+/DwBwcnJC27ZtK3ewRNXU4cOH4ePjg27duqFp06YwNjZGdnY2QkJCcPjwYVy7dg0AUKdOHfTt27eSR0tERFS9MZFERERUwUJCQuDp6Vnk8aZNm2L16tXckpzoA7x+/Rpbtmwp8ri1tTU2b94MFRWVjzgqIiKiTw+LbRMREVWgZ8+e4dSpU7h8+TLCw8MRFxeH7Oxs6OnpwdbWFu7u7ujatSsUFLjanKi8IiIicPLkSVy8eBEvXrxAXFwc0tPToauri/r166NDhw74+uuvmUQiIiKSAyaSiIiIiIiIiIioVLi0Tc6aNWuGzMxMGBkZVfZQiIiIiIiI6DMQHR0NFRUV3Lhxo7KHQp8BJpLkLCMjAzk5OZU9DCIiIiIiIvpMZGdnF7lLLJG8MZEkZ8bGxgAAf3//Sh4JERERERERfQ7at29f2UOgzwgrexIRERERERERUakwkURERERERERERKXCRBIREREREREREZUKE0lERERERERERFQqTCQREREREREREVGpcNe2SpSTk4OsrKzKHgZVU0pKSlBUVIRIJKrsoRAREREREdFngomkSiCVShEZGYn4+PjKHgpVc4qKijA2Noauri4TSkRERERERFThmEiqBPlJJGNjY2hoaDABQGUmlUqRnZ2NxMREREREIC0tDWZmZpU9LCIiIiIiIvrEMZH0keXk5AhJJAMDg8oeDlVz2traUFVVRUxMDIyNjaGoqFjZQyIiIiIiIqJPGIttf2T5NZE0NDQqeST0qdDU1IRUKmW9LSIiIiIiIqpwTCRVEi5nI3nh9xIRERERERF9LEwkERERERERERFRqTCRRCV69eoVJBIJfHx8ynxuYGAgJBIJAgMDK2BkFcvHxwcSiQSvXr0qsa+bmxs8PDw+wqiIiIiIiIiIKg8TSUREREREREREVCpMJBEVoUePHrhz5w7Mzc0reyhEREREREREVQITSVTt5ebmIiMjQ+5xFRUVoaqq+tGLWWdnZyMzM/OjXpOIiIiIiIioNJhIqgY8PT0hkUgQGhqKmTNnwsHBAY6Ojli7di2kUikiIiIwYcIENG3aFK1bt8a2bdsKxIiNjcUPP/wAJycn2NnZ4auvvsLhw4cL9EtMTISHhwccHBzQrFkzzJ49G0lJSYWOKyQkBFOnTkWLFi1gZ2eH3r17w9/f/4PuMSQkBN9++y2aNm2Kli1bYunSpQWSRBKJBIsXL4avry+6du0KOzs7XLhwAQDw4MEDjB49Gk2bNkWTJk0wbNgw3L59Wzj37t27kEgkhd77hQsXIJFIcO7cOQCF10iSSqX49ddf0aZNGzRq1AhDhgzBkydPCr2nxMRE/Pjjj3B1dYWtrS06duyILVu2IDc3V+iTX3/K29sbv//+Ozp06AA7OzuEhISU63UkIiIiIiIiqkhKlT0AKr3p06fDxsYG3333HQICArBx40bo6elh//79cHR0xMyZM3H06FH8/PPPsLOzQ/PmzQEA6enpGDJkCF68eIFBgwbBwsICfn5+8PDwQGJiIoYNGwYgL0kyceJE3Lx5E/3794eNjQ1Onz6N2bNnFxjLkydPMGDAAJiYmGDMmDHQ0NDAiRMnMGnSJHh6eqJjx47lusdp06bB3Nwc3333HW7fvo1du3YhMTERK1askOl39epVnDhxAoMGDYK+vj7Mzc3x5MkTDBo0CJqamhg9ejSUlJRw4MABDBkyBLt370ajRo1gZ2eHmjVr4sSJE+jVq5dMzOPHj0NXVxfOzs5Fjm/dunXYuHEjXF1d4erqivv372PkyJHIysqS6ZeWlobBgwfjzZs36N+/P8zMzBAUFITVq1cjOjoa//vf/2T6+/j4ICMjA3379oWKigp0dXXL9foRERERERERVSQmkqoRe3t7LF68GADQr18/uLm5Yfny5ZgxYwbGjh0LAOjWrRtcXFzw559/ComkAwcOICQkBL/88gu++uorAED//v0xZMgQrF27Fl9//TW0tLTg7++P69evY9asWRg9ejQAYMCAARg6dGiBsfz4448wMzPDn3/+CRUVFQDAwIEDMWDAAKxcubLciSQLCwts3LgRADBo0CBoaWlh7969GDlyJOrXry/0Cw0NxdGjR1GnTh2hbdKkScjKysK+fftQs2ZNAEDPnj3RuXNn/PLLL9i9ezcAwN3dHdu2bUNCQoKQsMnMzMSZM2fQsWNHKCsrFzq2uLg4/Pbbb2jbti02bdokLHlbs2YNNm3aJNN3+/btePnyJQ4fPozatWsDyHvNjY2N4e3tjZEjR8LMzEzoHxkZidOnT0MsFpfrdSMiIiIiIiL6GLi0rRr55ptvhMeKioqwtbWFVCqVadfR0YGVlRVevnwptP3zzz8wMjJCt27dhDZlZWUMGTIEqampuH79utBPSUkJAwYMkLnO4MGDZcYRHx+Pq1evokuXLkhOTkZcXBzi4uLw9u1bODs74/nz53jz5k257nHQoEEyz/Ov/c8//8i0N2/eXCaJlJOTg0uXLqFDhw5CEgkAjI2N0a1bN9y8eRPJyckA8hJJWVlZOHXqlNDv0qVLSExMhLu7e5Fju3z5MrKysjB48GCZukn5M7re5efnBwcHB+jo6AivT1xcHJycnJCTkyO85vm+/PJLJpGIiIiIiIioyuOMpGqkRo0aMs+1tbWhqqpaIAGhra2N+Ph44Xl4eDgsLS2hoCCbN7SxsQEAvH79WuhnZGQETU1NmX5WVlYyz1+8eAGpVIp169Zh3bp1hY41NjYWJiYmpb+5/2dpaSnzvFatWlBQUJCpUwTkzVx6V1xcHNLS0gqMFci7z9zcXERERKBu3bqoX78+rK2tceLECfTp0wdA3rI2fX19ODo6Fjm2/Ncpf4ZRPrFYXGApWlhYGIKDg9GqVatCY8XFxRV7P0RERERERERVERNJ1cj7iSAgb8ZQYaRSaYWNI79Y9MiRI+Hi4lJon1q1asnlWkXtmKampvZBcd3d3bFp0ybExcVBS0sLZ8+eRdeuXaGkJJ8fidzcXLRu3VpYIvi+95NRH3o/RERERERERB8DE0mfAXNzcwQHByM3N1cmGfXs2TMA/810Mjc3x9WrV5GSkiIzKyk0NFQmXv7SMWVlZTg5Ocl1rGFhYTJL08LCwpCbm1vijB2xWAx1dfUCYwXy7lNBQUGmJpG7uzu8vLxw6tQpGBoaIjk5GV27di32Gvmv0/Pnz2XGGBcXh4SEBJm+tWrVQmpqqtxfHyIiIiIiIqLKxBpJn4E2bdogOjoax48fF9qys7Oxa9cuaGhoCEW527Rpg+zsbOzbt0/ol5OTIxSpzmdgYIAWLVrgwIEDiIqKKnC995dtlcWePXtknudfu02bNsWep6ioiNatW8Pf319mGVxMTAz+/vtvODg4QEtLS2i3sbFBvXr1cPz4cRw/fhxGRkbC61AUJycnKCsrY/fu3TIzvnbs2FGgb5cuXRAUFIQLFy4UOJaYmIjs7Oxir0VERERERERUFXFG0megX79+OHDgADw8PHD//n2Ym5vj5MmTuHXrFn744QchweLm5oamTZti1apVCA8PR506dXDq1CkkJSUViLlgwQIMHDgQ3bt3R9++fVGzZk3ExMTg9u3biIyMhK+vb7nG+urVK4wfPx4uLi64ffs2fH190a1bN5kd24oybdo0XL58GQMHDsTAgQOhqKiIAwcOIDMzE7NmzSrQ393dHevXr4eqqiq++eabQpcOvkssFmPkyJHYvHkzxo0bB1dXVzx48AD//PMP9PX1ZfqOGjUKZ8+exfjx49GrVy80bNgQaWlpePz4MU6ePAl/f38W1yYiIiIiIqJqh4mkz4Camhp27dqFlStX4vDhw0hOToaVlRWWLVuG3r17C/0UFBSwceNG/PTTT/D19YVIJIKbmxs8PDzQs2dPmZh16tTBn3/+CS8vLxw+fBjx8fEQi8Vo0KABJk2aVO6xrl27FuvWrcOqVaugpKSEwYMH4/vvvy/VuXXr1sWePXuwatUqbN68GVKpFPb29vjll1/QqFGjAv3d3d2xdu1apKWloUuXLqW6xrRp06CiooL9+/cjMDAQ9vb22LZtG8aNGyfTT11dHbt27cLmzZvh5+eHv/76C1paWqhduzamTJkCbW3tUl2PiIiIiIiIqCoRSSuyKvNnqH379gAAf3//Qo+np6cjNDQUVlZWLLD8Dk9PT3h5eeHKlSucqVNG/J4iIiIiIvq8lfR3KJE8VakaSWFhYZg/fz569OiBBg0aoFu3bsX2P3PmDCQSSaH9kpKS8MMPP6BFixZo0qQJpk6dWmg9n1u3bqFfv36wt7dHu3btsGXLlgrd8YyIiIiIiIiIqLqqUomkJ0+eICAgAJaWlrCxsSm2b3p6On766ScYGhoWenzatGm4dOkSFi5ciJUrVyI0NBRjxoyRKXIcFhaGUaNGwcjICJs3b8awYcOwfv16bNu2Ta73RURERERERET0KahSNZLc3NzQoUMHAICHhwfu3btXZN/NmzejRo0asLCwKNAvKCgIFy9ehLe3N5ydnQEAVlZWcHd3x6lTp+Du7g4A8Pb2hr6+PlavXg0VFRW0atUKcXFx2LRpE4YMGQIVFZUKulMiIiIiIiIiouqnSs1IKmnXrHwvXrzA9u3bMXfu3EKP//PPP9DR0UHr1q2FNmtra3zxxRf4559/ZPq1b99eJmHk7u6OxMREBAUFlfMuqDymTJmC4OBg1kciIiIiIiIiqsKqVCKptH788Uf06NGjyC3hnz17BisrK4hEIpl2a2trPHv2DACQmpqKiIgIWFtbF+gjEomEfkRERERERERElKdKLW0rjbNnzyIoKAh+fn5F9klMTCx0e3VdXV1hGVxSUhIAQEdHR6aPiooK1NXVkZCQIMdRExERERERERFVf9UqkZSRkYGffvoJU6ZM+WyXQEmlUmGmVURMCh6/fIsXEUnIyMqGqrISaplpo15NfZgZahboT0RERERERET0IapVImnHjh1QUFBA165dkZiYCADIyspCbm4uEhMToaamBhUVFejo6CAyMrLA+QkJCdDV1QUAYcZS/sykfJmZmUhLSxP6VTUikQgBt17h6IVnCH7xtsh+klr66O5iDdemFh9xdERERERERET0KatWiaRnz54hLCwMrVq1KnCsefPmWLhwIQYMGABra2tcuXKlwGyc0NBQ1KtXDwCgoaEBMzOzArWQQkNDIZVKC9ROqgrexKVg3f7buBsSU2Lf4BdvEbznJk5eDcO3/RvDRKz5EUZIRERERERERJ+yalVse8yYMdi5c6fMP2dnZ5ibm2Pnzp1wc3MDALRp0wYJCQm4cuWKcG5oaCgePHiANm3aCG1t2rSBv78/srKyhLbjx49DR0cHTZo0+Xg3VgpPXsZj+pqAUiWR3nU3JAbT1wTgycv4ihkYEREREREREX02qtSMpLS0NAQEBAAAwsPDkZycLBTVbtGiBWxsbGBjYyNzzuHDh/HmzRu0bNlSaGvSpAmcnZ3xww8/YPbs2VBVVcWaNWsgkUjw5ZdfCv1GjRqFo0eP4rvvvsOAAQPw+PFjeHt7Y/r06VBRUfkId1w6b+JSsGDLZSSlZpXcuRBJqVlYuPUKVk9rw5lJRERERERERFRuVSqRFBsbi2+//VamLf/5zp07ZZJFJVm7di2WLVuG+fPnIzs7G87Ozpg7dy6UlP67ZUtLS3h7e2P58uUYO3YsxGIxpk6dipEjR8rnhj5Q/tK8dftvlzuJlC8xJRPr9t/GTxNbV0gBbl9fX+zduxfBwcEAAIlEgsGDB6Nbt25yvQ4RERERERERVR6RVCqVVvYgPiXt27cHAPj7+xd6PD09HaGhobCysoKamlqJ8QJuvcLKPTflNr6ZgxzkXoB7yZIl2LNnD77++mt06tQJIpEIJ0+exB9//IERI0Zg9uzZcr0eySrr9xQREREREX1aSvo7lEieqtSMJCro6IVnJXcqS7yLz+SaSPL398fu3bsxefJkTJkyRWh3cXGBsbExNmzYACcnJ7i4uMjtmkRERERERERUOapVse3PTURMCoJfvJVrzOCwt4iISZFbvB07dkBXV7fQ5YCjRo2Crq4ufv/9d9y4cQMSiQQvXrwQjo8fPx4SiQRPnjwR2mbMmIGxY8cKzzMzM7F69Wq0a9cOtra26NKlC44ePSpzHQ8PD3Tr1g2BgYHo2bMnGjdujG+++Qb37t2T230SERERERERERNJVdrjl/JNIuV7Iqe42dnZCAoKQsuWLaGpWbCIt6amJlq2bImbN2/C3t4eqqqquH79OgAgNzcXN2/elGkDgOvXr6NZs2bC82+//RYHDhzAiBEjsHnzZri4uGDWrFlCUfZ80dHRWLp0KUaNGoW1a9ciIyMDkydPltmRj4iIiIiIiIg+DJe2VWEvIpIqJG5YpHzivn37FpmZmTAzMyuyj5mZGdLS0pCcnAx7e3vcuHEDX3/9NYKDg5GWlobevXvj+vXrGDhwIMLCwhAVFYXmzZsDAK5evYqzZ8/C29sbzs7OAIDWrVsjOjoanp6ecHV1Fa6TkJCA3bt3o27dugAAdXV1DB06FP/++69MYoqIiIiIiIiIyo8zkqqwjKzsiombmVMhcUvSrFkzYfbR9evXYWtrizZt2si0qaurw9bWFgBw6dIl6OnpwdHREdnZ2cI/JycnPHz4EDk5/92HsbGxkEQCgDp16gAA3rx587Fuj4iIiIiIiOiTxxlJVZiqcsV8eVRVFOUSR19fHyoqKoiIiCiyT0REBFRUVCAWi9GiRQts3LgRb968wY0bN9CsWTM0a9YMMTExeP78OW7cuIFGjRpBWVkZQN6Mp/j4eDRs2LDQ2NHR0TA1NQUA6OjoyBzLj5GRkSGPWyUiIiIiIiIiMJFUpdUy066QuJam8omrpKSEpk2b4tq1a0hNTYWGhobM8dTUVFy7dk1YWta4cWMoKyvj+vXrwhI3PT091K1bF9evX8f169fRs2dP4XxdXV2IxWJs2bKl0OuLxWK53AcRERERERERlQ6XtlVh9WrqV0jcunKMO3ToUMTHx2Pbtm0Fjm3btg3x8fHo168fAEBDQwMNGjTAgQMHEB8fDwcHBwBA8+bN4evri1evXsnUM3JyckJcXByUlZVhZ2dX4J+Kiorc7oOIiIiIiIiISsYZSVWYmaEmJLX0EfxCfru3SSz1YWZYcIe18mrfvj0GDx4MLy8vREZGonPnzgCAU6dO4eDBg+jVq5fQBuTVSfL29kbDhg2hpaUltO3ZswfKyspo0qSJ0Ld169Zo164dRo8ejdGjR0MikSAtLQ1Pnz5FWFgYfvzxR7ndBxERERERERGVjImkKq67izWC99yUXzxna7nFyjdv3jzY29tj3759mDJlClJTUwEA06dPx7hx42T6tmjRAt7e3jIzj/J3abO1tYWamppM//Xr12PLli3Yt28fwsPDoa2tjbp166J3795yvw8iIiIiIiIiKp5IKpVKK3sQn5L27dsDAPz9/Qs9np6ejtDQUFhZWRVImrxPKpVCJBLhh18v4W5IzAePzb6OIX6c0FqIW1FiY2Px9ddfw8bGBps3b4aSEvOVFaks31NERERERPTpKenvUCJ5Yo2kKiw/2fNt/8bQ1lD+oFg6miqY2q+xTNyKYmBgAC8vL1y/fh0LFy6s0GsRERERERER0cfDqSLVgIlYE4vGOmHBlstISs0q8/k6mipYNKYVTMTyq41UEltbW9y5c+ejXY+IiIiIiIiIKh5nJFUTdWvqYc10V9jZGJbpPDsbQ6ye1gZ1aupVzMCIiIiIiIiI6LPBGUnViIlYEz9NbI2AW69w9OIzxCakoY6FHswMtaCspICs7FxExCTjyct4GOqpo7uzNVybWlT2sImIiIiIiIjoE8FEUjUjlUrh2tSi1Amiii6sTURERERERESfDyaSqrGst5HIeP0UmdEvIM3KgEhZFSpGtaBaow6U9U0re3hERERERERE9IlhIqmaEYlESL53AQnXjyHj9ZMi+6nWqAvd5l2hZevyEUdHRERERERERJ8yJpKqkaz4N4j++1ekh90rsW/G6yeIOrIWibfPwKjbRCjrmXyEERIRERERERHRp4y7tlUTGRFPEb7t+1Ilkd6VHnYP4du+R0bE0woaGRERERERERF9LphIqgay4t8gYt8S5KYll+v83LRkROxbiqz4N3IeWdXk4+MDiUSCuLi4yh4KERERERER0SeFiaQqTCqVAgCi//613EmkfLlpSYj++1eZuEREREREREREZcFEUhWWX1i7rMvZipIedg/J9y5AJBLJJR4RERERERERfV6YSKriEq4fk2+8G8flGg8A9u/fj3bt2qFRo0YYMWIEHjx4AIlEAh8fHwBAbm4ufv31V7i5ucHW1hadO3fG/v37C8S5fv06+vfvD3t7e7Rs2RJz5sxBfHy8TJ/IyEiMGzcOjRo1gqurK37//Xf8+OOPcHNzK3aMmZmZWL16Ndq1awdbW1t06dIFR48eldtrQERERERERPQ54K5tVVjW20hkvH4i15gZ4Y+R9TYSyvqmconn7++PBQsWoE+fPujUqRMePnyIadOmyfRZsWIFdu7ciQkTJqBJkyY4f/48FixYgOzsbAwePBgAcO/ePYwYMQItW7bEunXrEBMTg1WrVuHp06fYv38/FBUVIZVKMXHiRMTExGDRokXQ1taGt7c3Xr9+DQWF4nOi3377LW7duoVJkybBxsYGAQEBmDVrFnR0dODq6iqX14KIiIiIiIjoU8dEUhWW8bpidlrLeP1UbomkjRs3wtHREUuXLgUAuLi4IDs7G+vWrQMAxMXFYffu3Rg1ahSmTJkCAHB2dsbbt2+xYcMGDBgwAIqKiti0aROMjIywadMmKCsrAwDMzMwwatQoBAQEwM3NDf/88w/u37+PPXv2oFmzZgAAR0dHuLq6QkdHp8gxXr16FWfPnoW3tzecnZ0BAK1bt0Z0dDQ8PT2ZSCIiIiIiIiIqJS5tq8Iyo19U6bg5OTl4+PBhgWVl7du3Fx7fuXMHWVlZ6Ny5s0yfLl26IC4uDs+fPwcA3LhxA+3btxeSSEBewklHRwc3b94EANy9exc6OjpCEgkANDU10apVq2LHeenSJejp6cHR0RHZ2dnCPycnJzx8+BA5OTnlun8iIiIiIiKizw1nJFVh0qyMCoqbKZc4cXFxyM7Ohlgslmk3MDAQHickJAAADA0NZfrkP8+vgZSYmChz3rux8mNERUUVuBaAQtve9fbtW8THx6Nhw4aFHo+OjoapqXxmaBERERERERF9yphIqsJEyqoVFFdFLnHEYjGUlJQQFxcn0x4bGys81tPTE9pMTEyE9piYGJnjurq6Mue9G0tXVxcAYGxsXOBaAApte5euri7EYjG2bNlS5H0QERERERERUcm4tK0KUzGqVaXjKioq4osvvoC/v79M+5kzZ4THdnZ2UFZWhp+fn0yfEydOwMDAALVr1wYAODg4wN/fH9nZ2UKfS5cuITExEQ4ODkKsxMREXL9+XeiTkpKCK1euFDtOJycnxMXFQVlZGXZ2dgX+qajIJ7FGRERERERE9KnjjKQqTLVGnSofd8KECZg4cSLmzp2Lzp0748GDB/jrr78AAAoKChCLxRg8eDC8vb2hoqKCxo0bIyAgAH///TfmzZsHRUVFAMD48ePRv39/jBs3DkOGDBF2bbO3txeKYbdp0wYNGzbEd999hxkzZkBHRwe//fYbNDU1IRKJihxj69at0a5dO4wePRqjR4+GRCJBWloanj59irCwMPz4449yez2IiIiIiIiIPmVMJFVhyvqmUK1RFxmvn8gtpqp5Pbnt2AbkFdZeuHAhNm/eDF9fXzRq1AgLFy7EyJEjoaWlBQD4/vvvoa2tjT/++AObNm2Cubk5Fi1ahP79+wtxbG1tsW3bNqxevRpTpkyBhoYG3NzcMHv2bCHZJBKJ8Ouvv2L+/PmYP38+dHR0MHToUISGhuLhw4fFjnP9+vXYsmUL9u3bh/DwcGhra6Nu3bro3bu33F4LIiIiIiIiok+dSCqVSit7EJ+S/B3L3l/ulS89PR2hoaGwsrKCmppaifGS711A1JG1chufcY9p0LJ1kVu8whw6dAhz586Fv78/LCwsKvRamZmZ6Nq1K5o1a4Zly5ZV6LWqqrJ+TxERERER0aelpL9DieSJM5KqMKlUCi1bFyTePoP0sHsfHE/N0hZati6QSqXFLgUri/j4eHh5ecHR0RGampq4e/cuNm3ahPbt21dIEunAgQPIzc2FlZUVEhMThRlGq1evlvu1iIiIiIiIiEgWE0lVWH6yx6jbRIRv+x65acnljqWgrg2jbhNl4sqDkpISXr58ib///htJSUnQ19dHjx49MHPmTLld412qqqrYsmULwsPDAQD169fH5s2bYWdnVyHXIyIiIiIiIqL/MJFUDSjrmcBswDxE7FtSrmSSgro2zAbMg7KeidzHpqWlhc2bN8s9blF69uyJnj17frTrEREREREREdF/FCp7AFQ6qmZ1YD5yBdQsbct0npqlLcxH/gxVM5sKGhkRERERERERfS44I6kaUdYzQY3Bi5B87wISbhxHdmIsVM1soCw2g0hRCdKcbGTFRSAj4imUdAyh28y9wgtrExEREVHZeHh44OTJkwgKCirX+a9evUL79u2xbNky7kBLREQfHRNJ1Ux+Ae7SJojkWVibiIiIqDpJT0/H4sWLcefOHURERCA3Nxc1a9bE119/jYEDB0JZWbnEGKdOncLx48dx9+5dxMTEwNTUFO3atcPEiROho6Mj9AsMDMTQoUOLjDNt2jRMmDBBLvdVmICAANy5cwdTpkypsGsQEREBTCRVa5HJ0QiJfY4XCa+RmZMJFUUV1NKtARuD2jDVMqrs4RERERFVqvT0dDx9+hRt2rSBubk5FBQUEBQUhGXLluHOnTtYtWpViTHmzZsHY2NjfPXVV6hRowaCg4Oxe/duBAQE4PDhw1BTUwMA2NjYYMWKFQXO9/X1xcWLF9G6dWu53Ze5uTnu3LkDJaX/3soHBARgz549TCQREVGFYyKpmhGJRLgYdh0nnpzDk9jQIvvVNbBCl7rt4GzZ/COOjoiIiKjq0NPTw8GDB2XaBgwYAG1tbezevRseHh4wMir+w7f169ejZcuWMm22traYPXs2jh49ij59+gAADA0N0aNHjwLnb9iwAbVr14a9vf0H3s1/RCIRVFVV5RaPiIioLFhsuxqJSo7BonNrsP7qtmKTSADwJDYU669uw6JzaxCVHPORRkhERESVLTAwEL1794adnR06dOiA/fv3w9PTExKJRKbfkSNH0Lt3b9jb26NFixaYPn06IiIiZPoMGTIE3bp1w6NHjzB48GA0atQIHTt2hJ+fHwDg2rVr6NOnD+zt7dGpUydcvnxZ5vz864aGhmLmzJlwcHCAo6Mj1q5dC6lUioiICEyYMAFNmzZF69atsW3bNpnzMzMzsW7dOvTu3RsODg5o3LgxBg4ciKtXr37Qa2Rubg4ASExMLLHv+0kkAOjQoQMAICQkpNhz79y5g7CwMHTv3r3Q4y9fvsSoUaPQuHFjODs7w8vLC1KptMQxvXr1ChKJBD4+PgDyai7t2bMHACCRSIR/REREFYGJpGoiJC4MHqeX437U4zKddz/qMTxOL0dIXFgFjYyIiIiqigcPHmD06NGIj4/HlClT8M0332DDhg04c+aMTL+NGzdi9uzZsLS0hIeHB4YOHYorV65g0KBBBZIrCQkJGD9+POzt7TFr1iyoqKhgxowZOH78OGbMmAFXV1d89913SEtLw9SpU5GcnFxgXNOnT4dUKsV3332HRo0aYePGjdixYwdGjBgBExMTzJw5E7Vq1cLPP/+M69evC+clJyfj0KFDaNGiBWbOnInJkycjLi4Oo0ePxsOHD0v9umRmZiIuLg4RERE4ffo0tm3bBnNzc1haWpbxFc4TE5P3IZ2+vn6x/Xx9fQGg0ERSTk4ORo8eDQMDA8yaNQu2trbw9PTE+vXryzyefv36CUvnVqxYIfwjIiKqCFzaVg1EJcfgxwBPJGemlOv85MwU/BTgiWUdPWCsZSjn0VWMV69e4fDhw+jbty9MTEzkEtPHxwdz5szBlStXIBaLhR1P1q1bh86dO8vlGkRERJVp/fr1UFRUxL59+4Tfn126dIG7u7vQJzw8HJ6enpg2bRrGjx8vtH/55Zfo1asX9u7dK9MeFRWFVatWoVu3bgAAJycndOnSBd999x3279+PRo0aAcirETRq1CicOnWqwE5i9vb2WLx4MYC8pIebmxuWL1+OGTNmYOzYsQCAbt26wcXFBX/++SeaN89bmq+rq4uzZ89CRUVFiNW3b1906dIFu3btwk8//VSq1+X06dOYMWOG8NzW1hY//fSTTI2hsti6dSsUFRXRqVOnIvvk5OTgxIkTsLe3LzRhlZGRARcXF8ydOxcAMHDgQIwfPx5bt27FkCFDIBaLSz2eJk2aoHbt2rh06VKhy+uIiIjkiTOSqrD8qc0br+8qdxIpX1JmCjZe3yUTtyoLDw+Hl5cXoqKiKnsoRERE1UJOTg6uXLmC9u3by3wIY2lpCReX/3Z7PX36NHJzc9GlSxfExcUJ/wwNDWFpaYnAwECZuBoaGujatavw3NraGjo6OrCxsRGSSACExy9fviwwtm+++UZ4rKioCFtbW0ilUpl2HR0dWFlZyZyvqKgoJJFyc3MRHx+P7Oxs2Nra4sGDB6V+bVq2bInt27dj3bp16N+/P5SVlZGWllbq89919OhR/PHHHxgxYgRq165dZL8rV64gJiamyGVtADBo0CDhsUgkwqBBg5CVlYUrV66Ua2xEREQfA2ckVWH5hbXLupytKPejHuNi2HUW4CYiIvoExcbGIj09vdDZL++2PX/+HFKpFF9++WWhcd6fpWNqagqRSCTTpq2tDVNT0wJtQOF1h2rUqFGgr6qqaoFZN9ra2oiPj5dpO3z4MLZt24bQ0FBkZWUJ7RYWFsLjuLg45OTkCM81NDSgqakpPDc0NIShYd6s7M6dO2PTpk0YMWIETp06BSMjI6SnpyMpKUnmuoUV4b5x4wb+97//wdnZGdOnTy9w/F1Hjx6FoqKizGywdykoKKBmzZoybVZWVgDyPlArzX0RERFVBiaSqrgTT87JPZ68EkkeHh64d+8efvjhByxbtgxhYWGwt7fH8uXLoaWlhQULFuDChQsQi8WYMWOGzBup8+fPY8OGDQgODoaGhgY6deqE2bNnQ0NDA4GBgRg6dCgA2U8wg4ODkZqaipUrV+LSpUuIjIyEgYEBnJ2dMWvWLOEN7If466+/cODAAYSEhEAqlaJ+/fqYNWuWzE4rnp6e2LZtG/bs2YNFixbh4cOHsLGxwY8//ggbGxv89NNPOH78ONTV1TFy5EgMHz5cODcoKAibN2/GvXv3kJycDEtLS4wYMQI9e/b84LETERGVRm5uLkQiEbZu3QoFBYUCSSINDQ3hsVQqhaKiYqFximovbOazgkLBSfClOf/IkSPw8PBAhw4dMGrUKBgYGEBRURGbN2+Wmbn0zTffCMkXAJg8eTKmTJlSaHwA6NSpE9asWQN/f3/0798fx48fx5w5c2T6BAcHyzx/9OgRJkyYgLp162L9+vXFLotLT0/H6dOn0apVKyGBVR5lvS8iIqKPgYmkKiwyObrE3dnK6klsKCKTo2GqVfxWt6UVHR2N5cuXY8KECVBSUsLSpUsxc+ZMqKuro1mzZujbty8OHjyIWbNmoVGjRjA3N4efnx+mT5+O3r17Y8qUKYiOjsaqVauQmJiINWvWoGHDhpg/fz4WL16MZcuWwdraWrheeno6cnJyMH36dIjFYkRERGDTpk2YOHEidu3a9cH38+rVK/Ts2RO1atVCZmYmjh07hkGDBsHX11f4lBAAsrKyMHv2bAwfPhyGhoZYuXIlJk+ejKZNm8LAwABr166Fv78/li1bBnt7ezRt2hQA8Pr1azRt2hQDBgyAiooKbt26hblz50IqlaJXr14fPH4iIvp8GRgYQFVVFWFhBTfYeLetVq1akEqlsLCwQO2aFkgLu4eMiBBkxb6GNCcLCirqSMgMh6p5XSHJJJVKCyScPoaTJ0+iZs2a8PLykrn++wWpf/nlF2RkZAjP35/p8778vvmzkJydnbF9+/Yi+7948QKjR4+GWCzG1q1bS5wVdPbsWaSkpBS7rC03NxcvX76UeX8RGpr3vi9/V7my3FdlfH2IiOjzxERSFRYS+7xi4saFyS2RlJCQgN27d6Nu3boA8gpyLlmyBGPGjMGkSZMAAHZ2djh9+jTOnDmDoUOHYsWKFXB3d8ePP/4oxDEyMsLYsWMxceJE1K1bF3Xq1AEA1K1bF3Z2dkI/sViMRYsWCc+zs7NhYWGBgQMHIjQ0VObNWHlMnjxZeJybm4vWrVvjzp07OHz4sEyRzqysLMycOROurq5C3/Hjx6NRo0bCJ5qOjo7w8/ODn5+fkEh6t8aEVCpF8+bN8ebNGxw4cICJJCIi+iCKiopwcnKCv78/3rx5I9RJCgsLw4ULFwBAWNK2evVqrJ43EzOaakOa9t+SLqlUiqSMHOio5b1FTH8djhyoAZAC+PiJivxZS+8msv7991/cvn1bZrmcg4NDoefHxcVBX1+/QJLl0KFDAPKKbgOAsbExjI2NC40RHR2NkSNHQiQSwdvbu1RFsI8ePQp1dXV07Nix2H579uwRim1LpVLs2bMHysrKaNWqVbH3VRh1dXUAeUsLdXR0Sn0eERFRWTGRVIW9SHhdIXFfyjGusbGxkEQCIBSddHJyEtp0dHQgFosRGRmJ0NBQhIeH44cffkB2drbQp0WLFlBQUMC9e/dk4hXmr7/+wu+//46wsDCkpqYK7c+fP4eVlRVyc3ORm5srtCsoKBQ6pb4wISEhWL16NYKCghAbGysT+10KCgrCm7yi7ltRURG1atVCZGSk0JaQkABPT0/hTX5+3QM9Pb1SjY+IiKg4kydPxsWLFzFgwAAMGDAAubm5wgc+Dx8+hEgkgmF2HIY1r4VtV+/h1XMNtKqpDXVlRUQmZ+LKi0R0rifGNw3zlmNJszKQk5GK1zvnwrj7FCiLzT7q/bRt2xanTp3CpEmT0LZtW7x69Qr79+9HnTp1ZN4DFMXX1xf79+9Hhw4dULNmTaSkpODixYu4dOkS2rVrJ/O7vCijR4/Gy5cvMXr0aNy8eRM3b94UjhkaGqJ169Yy/ePj43HhwgV8+eWXxc5cUlVVxYULFzB79mzY29vjwoULOH/+PMaPH1+mHdvyNWzYEACwdOlSODs7Q1FRUeYDLCIiInlhIqkKy8zJrJC4Gdnyi/v+J17KysoAUKBekYqKCjIyMvD27VsAEGYrvS8iIqLY650+fRqzZ89Gv379MH36dOjp6SE6OhqTJk0Spn5v2LABXl5ewjmlrSeQnJyMkSNHQiwWw8PDAzVq1ICqqirmzp0rM60cANTU1GS2Ii7qvpWVlWXO9fDwQFBQECZNmoQ6depAS0sL+/btw4kTJ0ocHxERUUlsbW2xdetWrFixAuvWrYOZmRmmTp2KkJAQPHv2DEn/nkX0sY3oU08TNVRr4vDDWOy5Ew0AMNJURtMaWnCsWbDmYMarYIT/PgdmA+dD1dS6wPGK0rt3b8TExODAgQO4ePEi6tSpg19++QV+fn64du1aiec7ODggKCgIx44dQ0xMDJSUlGBlZYU5c+Zg8ODBpRrDo0ePAAC//fZbgWMtWrQokEjy8/NDVlYWunXrVmxcRUVF/Pbbb1i4cCF++eUXaGpqYvLkyUW+RyrJl19+iSFDhuDYsWPw9fWFVCplIomIiCoEE0lVmIqiSsmdykFVqWLilkb+zJv58+fLFLDOV9S08nx+fn744osvsHjxYqHt/TeSffv2Rdu2bUsdM9/t27cRGRmJzZs3o379+kJ7UlJSgZ1pyiMjIwPnz5+Hh4cHhgwZIrTv3bv3g2MTERHla9WqFQ4fPizTNnHiRBgb6CP62EZAmjdrt7WlLlpb6hYba0Wn/5JGuWlJiNi3BBajV+Hs2bOF9n+/QPWUKVMK/TBn+fLlWL58eYH29+sdikQijBs3DuPGjZNpf/f3fHHs7Oywbt26UvUtyvv3VJL+/fujf//+xfZ59/69vb3LPCYLC4sC41JUVMTcuXOFpXJEREQVhYmkKqyWbo2SO5VDzQqKWxrW1tYwNTXFy5cvMWjQoCL75c/weX8mUHp6unAs39GjR2Wem5iYCHUhyiI9PV3m2gBw69YthIeHl7jcrjQyMzORm5srEz85ObnIN+NERETlkZ6eDjU1NeH58+fP8U9AADrUMxSSSOWVm5qImBObYdp3TqUV4CYiIqLKxURSFWZjULti4ootKyRuaYhEInh4eGDmzJlITU1F27Ztoa6ujtevXyMgIADTp0+HlZUVateuDUVFRfz5559QUlKCoqIi7Ozs4OTkhMWLF2PDhg1o0qQJAgICcOXKFbmMrXHjxtDQ0MCiRYswduxYvHnzBp6enuVKShVGW1sbdnZ22Lp1K8RiMZSUlLBlyxZoaWkhLi5OLtcgIiLq0KEDevXqhZo1ayI8PBz79++HkqIIX9cruGStPFKf3EBqSBA0bJrIJR4RERFVL0wkVWGmWkaoa2CFJ7GhcotZ18BKbju2lVeXLl2go6ODTZs2CbOJzM3N4eLiAkPDvOKeYrEY8+fPx2+//QZfX19kZ2cjODgY/fv3x6tXr7B79254e3vD2dkZq1atQt++fT94XIaGhli3bh1WrFiBiRMnonbt2li0aFGhNRHKa9WqVZg/fz48PDygp6eHIUOGIDU1Fdu2bZPbNYiI6PPm4uKCY8eOITo6GioqKmjcqBH6mybBXPPDZiO9K/GmHxNJREREnymRVCqVVvYgPiXt27cHAPj7+xd6PD09Xdim/t1p50W5GHYd66/KL8kw1XEknC2byy0eVb6yfk8REdHnJTXkFiL3/yjfoCIF1J65CyJlVS5vIyKqAkr6O5RInkq3JzpVCqlUCmfL5mhoXE8u8RoaS+Bs2RzMHRIREX0+Ml6HyD+oNBcZkc+YRCIiIvoMMZFUheW/OZvQfAi0VDQ/KJa2iiYmNB8sE5eIiIg+fZmxryokblZMxcQlIiKiqo2JpGrAWMsQ/3OdUu5kkraKJv7nOhXGWoZyHhkRERFVednZFRJWmpNVIXGJiIioamOx7WrCRmyJ5R09sPH6LtyPegwAMFDXh7W4Fky1jKGsqISsnGxEJkchJC4McWnxAICGxvUwofkQJpGIiIg+UyJV9QqJq6BSMXGJiIioamMiqRox1jLEgnbTERr3Atpq2jDU0C+yb0zqWySlJ8FKXOsjjpCIiIiqGhVjy4qJa1K7QuISERFR1cZEUjUjlUqF5FBaRCSSnzxF6osXyM3IgIKqKjRq1YJW3TowNDOFoYY+pFIpayIRERFVoidPnmDz5s0IDAzE27dvoaenB0dHR4wfPx516tSp8Ourmdct8zmbr0fg7psUvEnOQlZOLoy1VNDGUgdfNzSEurIiRMqqUDGqVeT7jOvXr8Pb2xsPHz5EXFwcdHR0UL9+fUycOBEODg5Cv7S0NPj4+MDf3x+PHz9GSkoKLC0t0bdvX/Tr1w+KiopC38DAQAwdOlTmOrq6uqhduzYGDx6Mr776qsz3SURERGXHRFI1IxKJEB1wAa//Pobkx0+K7KdVry5qdOsKI1eXjzg6IiIietepU6cwY8YM6Onp4euvv4aFhQXCw8Pxxx9/4OTJk1izZg06dOhQYdeXSqVQs6gPZbEZsuIiSn3e45g02BproqONClQURQiJS8fBezEIikjBL52toNvQBSLFot9GPn/+HAoKCujfvz8MDQ2RmJgIX19fDB48GJs3b0abNm0AAC9fvsSSJUvQqlUrDB8+HFpaWrh48SIWLVqEf//9Fz///HOB2EOGDIGdnR0AID4+HidOnMCsWbOQlJSEQYMGlfEVIiIiorISSavQXvBhYWHw9vbGv//+iydPnsDa2hp///23TJ+ff/4Z//zzD16/fg2RSAQrKyuMHDkSXbt2leknkUgKxDc0NMSlS5dk2kJCQrB06VIEBQVBU1MTPXr0wLRp06CiolKue2jfvj0AwN/fv9Dj6enpCA0NhZWVFdTU1MoUO/3NGzz1/BUJd++V+hxdO1vUmTIRaiYmZboWVR8f8j1FREQV58WLF/jqq69gZmaGPXv2QCwWC8fi4uIwaNAgREZGwtfXFzVr1qzQsSTcOIHYk799UIw/78fgt5uRWO1ugw4ev0K1jEvb0tLS0KFDB9SvXx/e3t4A8l6H2NhY1K0rO2tqzpw58PHxwalTp2Bpmbc0L39G0rp169C5c2ehb2ZmJjp06IAaNWpg//79H3SPRETVVUl/hxLJU5Xate3JkycICAiApaUlbGxsCu2TkpKCPn36YN26dVi3bh2++OILzJgxA0ePHi3Qd8iQIThw4IDwb8uWLTLHExISMGzYMGRlZcHT0xPTp0/HwYMHsXz58gq5vw+R/PQp/v3u+zIlkQAg4e49/Pvd90h++rSCRkZERESF+e2335CWloYlS5bIJJEAQCwWY/HixUhNTYW3tzcePXoEiUQi8wfAvXv3IJFI0KtXL5lzR48ejT59+si0BQQEYODAgWjcuDGaNGmCsWPH4smTvJnLUqkUOk07Ye2tRPTa+wAxqVlYfC4MvfY+QL8DD7H1RgRyckv+XNFESznvQW2HMieRAEBdXR1isRhJSUkyr8P7SSQA6NixI4C8D/xKoqKiAl1dXSgpcaI9ERHRx1ClfuO6ubkJ07s9PDxw717BpMnixYtlnru4uODp06c4fPgwunfvLnPMzMwMjRs3LvJ6+/fvR0pKCry8vKCnpwcAyMnJwaJFizBu3DiYVJFZPOlv3uD+wiXITkou1/nZScm4v3ApGq36mTOTiIiIPpJz587B3NwczZo1K/R48+bNYW5ujnPnzmH+/PnQ0dHBjRs3hE+Vb9y4AQUFBTx69AjJycnQ0tJCbm4ugoKC0LdvXyHOX3/9BQ8PDzg7O2PmzJlIS0vDvn37MHDgQBw+fBgWFhaASATVml8g9+FLzD3zHBJDdYx2MEVQRDJ8HsTCTFsF3SQGMuPLyZUiOTMH2blSPI9Px87bUdBQUUKrQVNLXYMxOTkZmZmZePv2LY4cOYLHjx9j/PjxJZ4XExMDANDXL7ixSEpKCuLi4gDkfSj4999/4/Hjx/jxxx9LjEtEREQfrkrNSFJQKN9w9PT0kJWVVebz/vnnH7Rq1UpIIgFAly5dkJubW2AJXGXIX3X41PPXcieR8mUnJeGp568ycStKYmIiJBIJfHx8KvQ6VcGrV68gkUjg5+dXIfF9fHwgkUiEN8xERFQ9JCUlISoqCvXr1y+2n0QiQWRkJFJTU9G0aVPcuHFDOHbz5k106NABIpEIt27dAgAhqZSfnEpJScGPP/6IPn364LfffsPgwYMxZswYHDx4EFKpFJs3bxbiKappIDNHCtc6RpjuZIGuEjHmtq0FG7EaTj19W2Bsj2PT0P/gIwz+Ixhzz4QBikr4dYMXxIZGpd7I49tvv0WrVq3g7u6Obdu2oV+/fpg4cWKx52RmZmLHjh2wsLAQaiG964cffkCrVq3QqlUrdO7cGb/++iumT5+Ob775plRjIiIiog9TpWYklZZUKkVOTg5SU1Nx9uxZXLp0Cb/88kuBflu2bMHq1auhrq4OZ2dnfP/996hRo4Zw/NmzZ/j6669lztHR0YGRkRGePXtW4fdRkvzC2mVdzlaUhLv3EB1wgQW4q5G2bdviwIED0NHRqeyhEBFRGaSkpAAANDU1i+2XfzwlJQUODg5Yt24dUlNToaGhgZs3b2L69OkIDw/HzZs30aZNG9y4cQMikUjY+ezy5ctITExE165dZT50UFBQQKNGjRAYGFjgmmN+2oScy/uR9uw2AMDWWBP+z+IL9Kulq4qfOtRGeo4UIYqmCHqdiPScsr0OM2fOxMiRIxEREYG//voLWVlZyM7OhqqqapHnLFmyBE+fPsWWLVsKXa42adIkIZEWHx+Ps2fPYs2aNVBXV8ewYcPKNkAiIiIqs2qZSLpy5QpGjBgBAFBSUsK8efNkii4CQM+ePdG2bVsYGhri8ePH2LhxIwYOHIgjR45AV1cXQN7MmcL+QNfV1UVCQkLF30gpvP77mFzjRRw7zkRSNSIWiwvU1SAioqrv3QRRcVJSUiASiaCvr49mzZohOzsbt2/fhqmpKWJjY9GsWTM8ffpUmKl048YN1KlTR5hN/fz5cwAoMoGipaUl81xVVRXGlnUBy3lIeXIDiTf9oHU7CsmZBTNEWhrqaNPlS+g07wJVEyv4+vpi4sSJOHz4MOrXr4/MzMwC75fEYjEUFRWF51988YXw+KuvvkLv3r0xZ84crF+/vtDx/vbbbzh48CC+/fZbuLq6FtqnXr16cHJyEp67u7sjOTkZq1atQvfu3fl7k4iIqIJVy0SSvb09/vjjDyQnJ+Off/7B0qVLoaioKFN48t3tYps3bw4HBwf07t0bBw8exJgxYypj2GWWFhGJ5MdP5BozKfgx0iIioW5mKreYBw8exKZNmxAbG4vGjRtj5syZBfr4+Phg+/bteP78OfT09NC7d29MnTpV5s1mZGQkVq5ciQsXLiAtLQ12dnaYM2cObG1thT5ubm5o27YtatSogR07diAxMRFOTk5YtGgRjI2NhX5btmzBoUOHEBkZCU1NTdSvXx9LliwRdsXJzMyEl5cXjh49iujoaNSsWRMTJ04sUGcrKCgIa9aswZ07d6CoqIi2bdvihx9+gIGBbB2Jd92/fx+jRo1Cu3bt8OOPPxa5ZPP8+fPYsWMHHj16hIyMDNjY2GDKlCnClsj5r9ucOXNw5coVvjEmIqpGtLW1YWxsjODg4GL7BQcHw9TUFMrKyrC1tYWqqiquX7+OGjVqwMDAAFZWVmjWrBn27t2LjIwMYblbvvzl6itWrICRkVGB+O/+nn33uVQqhWbdZtCs2wx6ERrAnc0w6DwGyM6CSFUdKsa1oWpsCZGSsnBup06dMHv2bBw7dgz169dHUFAQhg4dKhPf398/ryZTIVRUVODm5oYtW7YgPT29wE6jPj4+WLlyJfr371/i8rf3OTo64ty5c7hz5w7atm1bpnOJiIiobKplIklLS0tYM9+qVSvk5ORg+fLl6N27d4E3TPnq168PKysr3L9/X2jT0dGR2TkkX0JCgjBrqTIlP6mYndaSnz6VWyLp3LlzmDdvHnr37g13d3fcv38f3377rUyf7du345dffsGwYcPg4eGBkJAQrFmzBjk5OULSKSEhAQMHDoSGhgbmzZsHbW1t7Nq1C8OGDcOpU6dkEjenT5+Gubk5Fi5ciMTERKxcuRJTpkzBgQMHAOQVHV23bh2mTp2Kxo0bIykpCTdv3pT5VPjbb7/FrVu3MGnSJNjY2CAgIACzZs2Cjo6O8AloUFAQhgwZAldXV6xZswZpaWlYu3YtJk6cKFzrfTdv3sS4cePQs2dP/O9//yu2hsSrV6/Qrl07jBw5EgoKCvjnn38wduxY7NixAy1btizfF4SIiKqMdu3a4cCBA7hx40ahBbdv3LiB8PBwjBgxAiKRCI9fJsLI3AYHff0hUtZFroYFJq44ixr6SsjMzMQfPn8hJiYGzZs3F2Lkf0BiYGAgM0unJDK/nxTz3g7qOsjO7n6/pmJmZiZyc3OF907169fH9u3bZfoUlsx6V3p6OqRSKVJSUmQSSWfOnMHcuXPx5ZdfYsGCBaW+j3w5OXkzqlJTU8t8LhEREZVNtUwkva9hw4bYsWMH4uLiSnwD8y5ra+sCtZCSkpIQHR0Na2treQ+zzFJfvKiYuGEvADmtbtu4cSOaNWuGZcuWAcjbRS8jIwO//ppX2Ds5ORnr16/H6NGjMWPGDABA69atoaysjOXLl2PUqFHQ19cXZhcdOnRISBq1atUKnTp1gre3N77//nvhmikpKdi6dSu0tbUBAKamphg+fDguXLgAFxcX3LlzBxKJBOPGjRPOeffT26tXr+Ls2bPw9vaGs7OzMKbo6Gh4enoKiaRVq1bB1tYWXl5ewhvuevXqoVu3bggICCgw5f7y5cuYNGkShgwZItxrcQYPHiw8zs3NRcuWLfH06VMcPHiQiSQiok/AqFGj4OvriwULFmD37t0yO5DFx8djwYIF0NLSQiPHTpj8y1mERSYhVcEUb19fgKKKJvSt2+DlmyS8fAOoaBlj+UpPAICtXWMhjouLC7S0tLB582a0bNkSysrKMmOIi4srcUbr+x96JCYmQl1dvUCsQ4cO5V3//2cK6+rqFpm8io2NLTB7NzExEadOnYKZmZnMsevXr2PGjBlo1qwZVq5cWa7NV86fPw8gr3g5ERERVaxPIpF08+ZNaGlpFbpFbL6HDx8iNDQUvXv3FtratGmDTZs2ydRK8vPzg4KCAlq3bl3h4y5JbkZGBcXNlEucnJwc3L9/H7NmzZJp79Spk5BICgoKQmpqKjp37ozs7Gyhj5OTE9LT0/HkyRO0aNECly5dQsuWLaGrqyv0U1BQQPPmzXH37l2Z+C1bthSSSACEnff+/fdfuLi4oEGDBti7dy+WLVuGjh07olGjRjJvhi9dugQ9PT04OjoWGNPChQuRk5ODzMxM3Lp1C99//73wKScA1K5dG2ZmZrh7965MIun8+fM4duwYJk+eLJPAyn+d3v1UN79waGRkJNasWYPLly8jOjpa6NOwYcPSvPxERFTFWVpa4ueff8Z3332H7t2745tvvoGFhQXCw8Pxxx9/IDExEW69J+PXo/99cKQutkLc07PITo+HukFtmfaEF4FQUtfHkl0PMHOwBr6oLYaWlhYWLlyI77//XpgdLBaL8fr1awQEBKBp06aYP39+mcZ97do1LF26FJ06dYKlpSWysrJw8+ZNnDp1Cra2tvjqq69KjDFmzBiYmJigUaNGMDAwwOvXr+Hj44OoqCisWbNG6BceHo4JEyZAJBKhU6dOOHHihEwciURSYOe7GzduIOP/3yMlJCTg7NmzuHbtGrp27QobG5sy3SsRERGVXZVKJKWlpSEgIABA3huL5ORkYVv1Fi1aICoqCitXrkTnzp1hbm6O1NRUnD9/HocOHcKMGTOEP9C9vb3x4sULtGzZEmKxGE+ePMGmTZtgamoqU0epf//+2LVrFyZNmoRx48bhzZs3WLFiBfr37w8TE5OP/wK8R6GYHU0+LK6KXOLExcUhOzu7wCedhoaGwuO3b/O2E+7Vq1ehMSIiIoR+t2/fLjSJUqtWLZnnhdUnEovFiI6OBgD07t0bKSkpOHjwIH7//Xdoa2ujZ8+emDlzJtTU1PD27VvEx8cXmbCJjo6GSCRCTk4Oli1bJsy2Kmzc+c6dOwd1dXW4u7sX6NuxY0eEh4cLz/39/VGjRg1MmDABSUlJmDp1KiwtLaGuro7169cXiE1ERNVXp06dYGVlhc2bN+OPP/5AbGwscnNzoaqqinYD5uFpjOzvZHWxJSBSgIKiMlR1arzTnpdIUhdbIeptGuZtuoz5o1vCvo4RunfvDmNjY2zZsgXe3t7IzMyEiYkJmjVrJvMBWmnVq1cPLVu2hL+/v/BBR61atTBp0iSMGjUKKiolv4/4+uuvcezYMfz+++9ISkqCjo4OGjVqhFWrVsks83v16pWwVG7x4sUF4kyePLlAImnXrl3CY2VlZdSsWRPTp0/HqFGjynyvREREVHZVKpEUGxtboL5O/vOdO3fCxsYGOjo6+PXXXxEdHQ1tbW1YW1vDy8tLZumSlZUVTp06hRMnTiAlJQX6+vpwdXXFtGnTZHZp09XVxY4dO7BkyRJMmjQJmpqa+OabbzB9+vSPc8Ml0HgvgSK3uJbyiSsWi6GkpCSz3TAAxMTECI/za015eXnB1LRgXab8gpy6urpwcXEp8PUHUOANa2xsbIE+7y5rVFBQwLBhwzBs2DC8efMGx44dw6pVq6Cvr49JkyZBV1cXYrEYW7ZsKfK+srOzIRKJMG7cOJnvrXzvz37z8PDAwYMHMXz4cOzZs0fmXjdu3IjMzP9mgRkbGyMsLAwPHjzAhg0bZOKnp6cXOiYiIqq+6tWrh1WrVgHIqzt05MgRzPbwwKXTPjBr0l+mr4KSGup1XV4gho5FU+hYNBWeZ2Tl4Mft17BhlhsM9dTRsmXLEpdFL1++HMuXF4w9ZcoUTJkyRXheq1YtmU1LymPQoEEYNGhQif1atmxZYkHy8vQlIiKiilOlEkkWFhYlvkFYvXp1iXHc3Nzg5uZWqmva2Njg999/L1Xfj02rbp2KiVtHPnEVFRXRoEEDnD59GsOHDxfaT548KTxu0qQJ1NXVERkZiY4dOxYZy8nJCb6+vrCxsYGGhkax1w0MDERSUpKwvO3KlSuIj49Ho0aNCvQ1MTHByJEj8ffffwv1sJycnPDbb79BWVm5wKec+VRUVNC4cWM8e/ZMKOxeHHV1dWzdulVIYO3Zs0eYmVVYvYb8KfnvLrkLDw9HUFAQateuXeL1iIioehKJRLBq2BqG9Tsj5uEJKKvrwrB+l3LFSk3PxvqDQVg81glSqbTYDR6IiIiI5KVKJZJIlrqZKbTq1UXy4ydyi6ktqSe3HdsAYPz48Zg4cSLmzJkj7Np25MgR4biOjg6mTp2KX375BZGRkWjRogUUFRXx8uVL+Pv7w9PTE+rq6hg+fDiOHj2KwYMHY+jQoahRowbi4uLw77//wsTERCZRpampiTFjxmDMmDFISkrCypUrYW9vDxeXvAri8+fPh46ODho3bgwdHR3cunULjx49woABAwDkFdZu164dRo8ejdGjR0MikSAtLQ1Pnz5FWFgYfvzxRwDA999/j2HDhmHatGno2rUrdHR0EBkZicuXL6N3794FPvnV0tLCtm3bMHToUIwYMQI7d+4ssm6XtbU1TE1NsWrVKuTm5iI1NRXr16+HsbGx3L42RERUNe068RBim3YQ27T74FhBwdG4FxIDWxvDkjsTERERyQETSVVcjW5d8Xj1WrnFM+tasIbPh2jfvj0WLVqETZs24dixY2jUqBHWrl0rU4tq5MiRMDExwfbt27F7924oKSmhVq1aaNu2rTAjR19fHwcOHMDatWuxcuVKxMfHw8DAAI0aNSowk6ljx44wNTXFggULkJiYCCcnJyxatEg43qRJExw8eBCHDh1CWloaatasiTlz5siMaf369diyZQv27duH8PBwaGtro27dujK1JJo2bYq9e/fC09MTc+bMQVZWFkxNTeHo6AhLS8tCXw9dXV1s374dgwcPxqhRo7Bjxw6ZwuD5VFRU4OnpicWLF+Pbb7+FmZkZJkyYgKtXr+LevXvl+2IQEVGVFxIej+Cwt3KNefzycyaSiIiI6KMRSd/dToo+WPv27QHkFVQuTHp6OkJDQ2FlZQU1NbViY+VPU783dwES7n54ckHXzha2SxdV6+nvbm5uaNu2bZl3oPmUleV7ioiIKtfBM4+x68RDucbUUFPCgR+7yjUmERFVLyX9HUokTwqVPQAqWn6yp86UiVDS1vqgWEra2qgzZaJMXCIiIvq4nr6Kl3vM1PRshEcnyz0uERERUWGYSKoG1ExM0HDhvHInk5S0tdFw4TyomZjIeWRERERUFlFxqRUS900FxSUiIiJ6H2skVRNadeqg0aoVeOr5a5mWuena2aLOlImfTBLp7NmzlT0EIiKicsutoIoC0lxWKiAiIqKPg4mkakTNxAS2SxchOuACIo4dR0ZsLLRsbKBewwwiJSVIs7OR9joCyU+fQtXQEGZd3WHk6lLZwyYiIqL/p6OpWjFxtSomLhEREdH7mEiqZqRSKYxcXUqdIKrOhbWJiIg+NTYWuvj3SbRcYyopilDbTJu/84mIiOijYCKpGouLScHrl/GIikhCVlYOlJUVYWymjRo19SA21Kzs4REREdF7bK0N4HPuqVxj1qulD2UlRbnGJCIiIioKE0nVjEgkwt1b4bh2IRThL+KL7GdeSw8tXKxg19T84w2OiIiIiiSVSuFQ3wRGeuqIjk+TW9xOjrXlFouIiIioJNy1rRp5G5eKnb9eweE9QcUmkQAg/EU8Du8Jws5fr+Atd3IhIiKqdCKRCAoKIvRqW0duMU3EGnBuVAPSCiriTURERPQ+JpKqidcv4/Hbmgt4HhJbpvOeh8TitzUX8PplfMUMjIiIiEpNKpWiq7MVvqgt/uBYIhEwtV9jqCgrsjYSERERfTRMJFUDb+NSsWdLINJSs8p1flpqFvZsDawyM5OGDBmCcePGlbp/YGAgNm3aVOnjAIBXr15BIpHAz89P7uMhIqJPn0gkgoJIhO8GOUCso/ZBsQZ8KYF9HSPORiIiIqKPiomkKiz/jeHR/f+WO4mULy0lC0f3/ysTt7IsWLAAs2fPLnX/a9euYfPmzRU4otIzNjbGgQMH4OjoWNlDISKiasxErIFlE1vDzKDsm2MoiIAhXb7AgC/rc6c2IiIi+uiYSKrC8gtrl3U5W1Geh8Ti7q3wSn/DWadOHVhbW1dI7PT09AqJm09FRQWNGzeGnp5ehV6HiIg+fTWMtLB+Zlt0c7aCQil/NZsbaWHZJGf07VCPSSQiIiKqFEwkVXHXLoTKNd71i/KL5+HhgW7duuHy5cvo3r077O3tMXjwYLx69Qrx8fH49ttv0bRpU3To0AHHjx8Xznt/SVlkZCS+/fZbODk5wc7ODm5ubvjpp58AAJ6envDy8kJqaiokEgkkEgmGDBkiHGvSpAnu3LmDfv36wc7ODnv27AEArFy5Et27d0eTJk3g4uKCGTNmICoqqtj7CQkJwfTp0+Hq6opGjRrB3d0d27ZtQ25urtCHS9uIiEieVJUVMa6XPbb80BHfuNWFhbFWgaSSproymn9hgv+NaIFfv3dDAysDAGASiYiIiCqFUmUPgIoWF5NS4u5sZfUqLB5xMSkQG5Z9Kn1hoqOjsXz5ckyYMAFKSkpYunQpZs6cCXV1dTRr1gx9+/bFwYMHMWvWLDRq1Ajm5uYFYnz//feIiorC3LlzYWBggIiICNy7dw8A0KdPH0RGRuLvv//Gjh07AABaWlrCuVlZWfjuu+8wfPhwTJ8+XZgpFBsbi3HjxsHY2BhxcXHYvn07hgwZgmPHjkFJqfBv+6ioKFhZWaF79+7Q1NTEw4cP4enpidTUVEyePFkurxcREdG78pNBJmINDOvaAMO6NkBaejYi41KQkyOFtoYyTN5Z/lbZy9OJiIiImEiqwipqp7XXL+PllkhKSEjA7t27UbduXQB5yZglS5ZgzJgxmDRpEgDAzs4Op0+fxpkzZzBs2LACMe7evYsZM2bA3d1daOvZsycAwNTUFKamplBQUEDjxo0LnJuVlYXp06fLnAsAy5YtEx7n5OSgSZMmaNOmDa5evQpnZ+dC76VVq1Zo1aoVgLw36g4ODkhPT8fu3buZSCIioo9GTVURVjV0Cz3GWUhERERU2ZhIqsKiIpIqJG50pPziGhsbC0kkAKhduzYAwMnJSWjT0dGBWCxGZGRkoTEaNGiAbdu2QVFREa1bt4alpWWZxuDq6lqgLSAgABs3bsSTJ0+QnJwstD9//rzIRFJGRgY2b96Mo0ePIiIiAllZ/xU4T0lJgaamfJJvRERExWGyiIiIiKoy1kiqwrKyciombqb84uro6Mg8V1ZWBgBoa2vLtKuoqCAjI6PQGGvWrIGjoyPWrl2LL7/8Ep07d8apU6dKdX11dfUCCZ47d+5g4sSJMDY2xooVK3DgwAEcPHgQAIocAwD88ssv8Pb2Rp8+fbBlyxb88ccfmDBhQonnEREREREREX0uOCOpClNWVqyYuCoVE7e8jI2NsWzZMuTm5uLevXvYuHEjpk+fDj8/P9SsWbPYcwv71PbMmTPQ0tLC2rVroaCQlysNDw8vcRx+fn7o168fxo4dK7QFBASU8W6IiIiIiIiIPl2ckVSFGZtpl9ypHIxMKybuh1JQUIC9vT2mTZuG7OxshIWFAcib5ZSZmVnqOOnp6VBWVpZJMh09erTE8zIyMoQZVUBebaVjx46V4Q6IiIiIiIiIPm2ckVSF1aipV63ilkdSUhJGjRqFHj16wMrKCllZWdi1axd0dHTQoEEDAICNjQ2ys7OxY8cONGnSBFpaWrC2ti4yZuvWrbFjxw4sWbIEHTt2RFBQEI4cOVLiWJycnHDo0CHUqVMH+vr62Lt3b5kSWERERERERESfOiaSqjCxoSbMa+kh/EW83GJaWOrJbcc2eVBVVUW9evWwa9cuREREQE1NDba2tvD29oZYLAYAtGvXDgMHDsSWLVsQGxuL5s2bY9euXUXGdHV1xcyZM7F79274+PigadOm2Lx5Mzp16lTsWObNm4cFCxZgyZIlUFdXR69evdCxY0fMnTtXrvdMREREREREVF2JpFKptLIH8Slp3749AMDf37/Q4+np6QgNDYWVlRXU1NRKjHf3VjgO7wmS2/h6DWoCu6bmcotHla+s31NERERERPRpKenvUCJ5Yo2kKkwqlcKuqTlq2xjIJV7tOgawa2oO5g6JiIiIiIg+L4GBgZBIJPDz86vsoVA1x0RSFZZfLLp7/0ZQ11AuoXfx1DWV0b1fI5m4REREREREn7Jbt27B09MTiYmJlT2UMgkJCcGKFSvQo0cPNGnSBM7Ozhg7dizu3r1baP+cnBzExsaiWbNmaNq0KSZMmICXL19+5FHT54KJpGpAX6yBQWNbljuZpK6pjEFjWkJfrCHnkREREREREVVdQUFB8PLyqnaJpD/++AOHDh2Cra0tPDw8MHz4cISGhqJfv364fPmyTN+UlBRER0cjIyMD48aNw9SpU/Hw4UMMHjwYb9++raQ7oE8Zi21XEzVq6mH0dBcc3f8vnofEAgB09NRgZqELsaEmlJQUkJ2di7iYFLx+mYCkhHQAQG0bA3Tv34hJJCIiIiIiomqia9eumDx5MjQ1/9so6euvv4a7uzs8PT3h5OQktO/duxfZ2dkwNjbGmDFjAAAuLi7o3r07tm/fjhkzZnz08dOnjYmkakRfrIGhE1sh4lUCNLRUoKunXmTfhPg0pCZnwsxC9yOOkIiIiIiI6MMEBgbi559/xpMnT2BiYoLRo0cjOjoaXl5eCA4OBgBIJBIMGjQIjRs3xoYNG/D69WvUrVsXc+bMQfPmzQEAnp6e8PLyAvBfMWogryC1hYVFsWPYs2cP9u7di7CwMOjp6aFjx46YPn06dHR0hD7Pnz/HqlWrcOvWLSQmJkJfXx8ODg5YvHgxtLW1AQCXLl2Cl5cXnjx5gpycHBgbG6NTp04lJndsbW0LtOnr66NZs2a4du2aTPvJkyehrKwMFRUVoc3GxgatWrXCiRMnClwrNzcXq1evxp9//omUlBQ4OjpiwYIFMDMzK3ZMRPmYSKpmpFKpkBxKiI3Cm1fPEBsZjuysTCgpq8DA1BwmFtbQNTCGrp46pFIpayIREREREVG18ODBA4wePRpGRkaYMmUKcnNzsWHDBojF4gJ9r1+/juPHj2PIkCFQUVHBvn37MHr0aBw6dAj16tVDx44d8fz5c/z999+YM2cO9PX1AaDQWO/KT0A5OTlhwIABCA0Nxb59+3D37l3s27cPysrKyMzMxKhRo5CZmYnBgwfD0NAQb968wfnz55GYmAhtbW08efIE48aNg0QiwdSpU6GiooKwsDDcunWr3K9PdHQ09PT0hOe5ubkIDg6WSSLls7Ozw8WLF5GcnAwtLS2hfePGjRCJRBgzZgxiY2OxY8cODB8+HEeOHOEu0FQqTCRVMyKRCI9vX8W/l0/jzctnRfYzqWmNRk4dUa+x40ccHRERERERUfmtX78eioqK2LdvH0xMTAAAXbp0gbu7e4G+jx8/xp9//inM3unatSs6d+6M9evXw8vLC/Xr10eDBg3w999/o0OHDiXOQgKAuLg4bN68Gc7Ozti6dSsUFPLKCltbW2Px4sXw9fXF119/jZCQELx69Qrr1q1D586dhfMnT54sPL506RKysrKwdevWEpNXpXHjxg3cvn0bEyZMENri4+ORmZlZaALIyMgIABAVFSWTSEpISMDx48eFtgYNGmDatGk4ePAghg4d+sHjpE8fi21XI4lx0Ti89WecOrC52CQSALx5+QynDmzG4a0/IzEu+iONkIiIiIiIqHxycnJw5coVtG/fXkgiAYClpSVcXFwK9G/SpInMErAaNWqgffv2uHjxInJycso1hsuXLyMrKwtDhw4VkkgA0KdPH2hpaSEgIAAAhCTMxYsXkZaWVmis/GVw/v7+yM3NLdd48sXGxuK7776DhYUFRo8eLbRnZGQAKHxnblVVVZk++Xr27CmTWOrcuTOMjIyEeyMqCRNJ1UTUq1Ac3LAI4c8elem88GePcHDDIkS9Cq2gkREREREREX242NhYpKenw9LSssCx0rbVrl0baWlpiIuLK/Za8fHxiI6OFv4lJSUBAF6/fg0gbwbSu1RUVFCzZk2Eh4cDAGrWrIkRI0bg0KFDcHR0xKhRo7Bnzx4hDgC4u7ujadOmmDt3LpycnDB9+nQcP35cJqn07hiio6ORnp5eYKypqakYN24cUlJS8Ouvv8oU4M5PFkml0gLn5SeQ8vvke/91E4lEsLS0FO6NqCRMJFUDiXHR8N2+CumpKeU6Pz01Bb7bV3NmEhERERERfTIKS56U1pQpU+Ds7Cz8+/HHH8scw8PDA76+vhg3bhzS09OxdOlSdO3aFZGRkQAANTU17NmzB7///jt69OiB4OBgTJ8+HSNGjBBmTL07BmdnZxw/flzmGpmZmZgyZQqCg4Px66+/ol69ejLH9fT0oKKiUugMrOjovL//jI2Ny3xvRMVhjaQqLL9Qtv+f28qdRMqXnpoM/z+3odeY2SzA/f8kEgm+//57jBo1qrKHQkRERET02TMwMICqqirCwsIKHCus7cWLF4gIe4qnd68jKjwUb6Mj4R8QBGUlRVw+uhM1atdDZnpqodeaPXs2EhMThef5yZYaNWoAAJ49e4aaNWsKxzMzM/Hq1Ss4OTnJxJFIJJBIJJg4cSJu3bqFAQMGYN++fZg+fToAQEFBAa1atUKrVq0wZ84cbNq0CWvWrEFgYCCcnJywfft2mXh16tQRHufm5mL27Nm4cuUK1q5dixYtWhS4DwUFBdSrV0/Yze5dd+7cQc2aNWWWsRX2WkqlUoSFhUEikRT6WhG9j4mkKiy/sHZZl7MVJfzZIzy+fZUFuP/fgQMHhF8URERERERUuRQVFeHk5AR/f3+8efNGqJMUFhaGCxcuAIDMh+JBQUHYuGwOjPXzEiVJqRl4+ioGliZ6ePnkHl4+uYd7oW8AAG8iwmFhYSGc/25tpXc5OTlBWVkZu3btQps2bYRr/fHHH0hKSoKrqysAIDk5GWpqalBS+u9P6nr16kFBQQGZmZkA8pbPvbvDGgB88cUXACD0eT8x9a4lS5bg+PHjWLx4Mb788ssi+3Xq1An37t0TYgJ5ibCrV69i5MiRBfr/9ddfGDt2rJBg8vPzQ3R0NMaMGVPkNYjexURSFffv5dNyjXfnyhkmkv5f48aNK3sIRERERET0jsmTJ+PixYsYMGAABgwYgNzcXOzevRt169bFw4cPIZVKcfHYfgCAgY4Gjlx6gEY2ZlBUVMDdZ3lLylo2+G8mkZGuBgDgf99PR6/e38DCWoK2bdvK1Bl6l1gsxrhx4+Dl5YXRo0fDzc0NoaGh2Lt3L+zs7PDVV18BAK5evYrFixejc+fOqF27NnJycnDkyBEoKiqiU6dOAIANGzbgxo0bcHV1hbm5OWJjY7F3716YmprCwcGh2Nfh999/x969e9GkSROoqanhyJEjMsc7duwIDY28exs4cCDWrl2LmJgY/Pbbb1BSUsLvv/8OAwODQhNJurq6GDhwIHr37o3Y2Fjs2LEDlpaW6Nu3b4lfHyKAiaQqLSE2qsTd2coq8kUIEmKjoGsgv3Wyp06dwoYNG/Ds2TPo6uqia9eumDFjBlRVVREYGIihQ4diy5YtOHToEC5evAgtLS0MHjwY48ePl4kTEhKClStX4tq1a8jJyUGLFi0wd+5c1KpVS+gjkUgwc+ZMpKenY9++fcjJyYGbmxvmzZsn/EcaFRWFNWvW4Nq1a4iOjoapqSk6d+6MyZMnQ0VFRSYWl7YREREREVUdtra22Lp1K1asWIF169bBzMwMU6dORUhICJ49e4bTB7fgyb+BAIAahjowE2vj2qOXSErNgFhbAx0c6sBQ978kkYlYG44NauLuszdY47UZUuTtolZUIgnIq58kFouxe/duLFu2DLq6uujbty9mzJgBZWVlAHl/Szg7O+PcuXN48+YN1NXVIZFIsHXrVuEDazc3N4SHh+PPP//E27dvoa+vjxYtWmDKlCnQ1tYu9nV49ChvVUpQUBCCgoIKHPf39xf+/tHS0oKRkRESEhKwceNG5ObmomXLlpgzZw7EYnGBc8ePH4/g4GBs2bIFKSkpaNWqFRYsWAB1dfVix0SUTyT9kAplVED79u0B5P1gFyY9PR2hoaGwsrKCmppasbEe/3sVp/ZvlvsYv+w/HvUatZRLLH9/f0yaNAldu3ZFjx498OzZM6xZswaurq5Yv369kEgyMTFB165d4eTkhMuXL2Pbtm1YuHAhBgwYAAB4+fIlevfujbp162LEiBEQiUTYtGkT4uLi4OfnJySAJBIJzMzM4ODggB49euD58+dYsWIFhg8fjpkzZwIAgoOD8ddff8HBwQE6Ojp4/vw5PD094ezsjGXLlglj/1QSSWX5niIiIiIiqo4mTpyIe3f+RV+XvGLTnj6XYWdtiraNrUs4U5ZIQQHfjP8fTGqW7byqrqS/Q4nkiTOSqrDYyIrZfjHujfzienl5oXHjxli1ahUAoE2bNlBXV8f8+fNlCr45Ojpi9uzZAAAXFxfExsZi48aN6NevHxQUFODl5QVdXV1s375d2J6yadOmaN++PQ4dOoRBgwYJsYyMjGSu9+DBA5w8eVJIJEkkEuFa+XHU1dXh4eGB+fPnM9NORERERFSFpaeny3xA+vz5cwQEBEBS0/CDY0tzc3HmD2/0n7IQCopK3ISIqBwUKnsAVLTsrMySO5UrboZc4qSkpODhw4fCGuB87u7uAICbN28KbR07dpTp06lTJ7x580bYGvPSpUtwc3ODoqIisrOzkZ2dDR0dHTRo0AD37t2TOff9gnQ2NjZCHCCvAN/vv/8Od3d32Nvbo2HDhpg5cyays7Px8uXLD79xIiIiIiKqMB06dMCqVatw8OBBrFmzJu/DZ5EITeuaySX+26jXeHz7KpNIROXEGUlVmJKySsmdyhVXVS5xkpKSIJVKYWBgINOura0NFRUVJCQkCG3vr801NMz7NCE6Oho1atTA27dvsWPHDuzYsaPAdfLXIefT0dEpcPzdHQp27NiBn3/+GaNHj0bLli2ho6ODu3fvYvHixcjIkE8SjYiIiIiIKoaLiwuOHTuG6OhoqKiowM7WFrU1M6CnJb+VBXcDz+GLZi5yi0f0OWEiqQozMDWvkLhiE/nE1dbWhkgkQlxcnEx7UlISMjMzoaurK7S93ycmJgZA3jI1IG/nAFdXVwwcOLDAdYorhFcYPz8/uLm54bvvvhPaQkJCyhSDiIiIiIgqx7t1TQHg0a1LOHPoN5m2Kb1lVymUVdSrUKSlJEFNQ4szk4jKiEvbqjATi4opAGdiYSWXOJqamvjiiy/g5+cn037ixAkAkNnS8vTp0zJ9Tp48CWNjY5iamgIAWrVqhSdPnqBBgwaws7OT+WdtXbbXIT09vcAspqNHj5YpBhERERERVQ1R4WEVEjc6PIxJJKJy4IykKkzXwBgmNa3x5uUzucU0rWUDXQNjucWbPHkyJk2ahJkzZ+Krr75CaGgo1qxZg06dOkEikSAwMG9rzqtXr+Lnn39G69atcenSJRw5cgTz58+HgkJeLnPq1Kn45ptvMGrUKPTt2xeGhoaIiYnBtWvX0KxZM3Tr1q3UY3JycsLOnTuxe/du1K5dG76+vggLq5hfPkREREREVLGS4mOqVVyiTx0TSVVcI6eOOHVgs9zi2bfqILdYQN42k+vWrcOGDRswceJE6OnpoW/fvjLLygBg8eLFOHDgAPbt2wdNTU18++23MjuxWVpa4tChQ1i7di0WLVqE1NRUGBkZoXnz5pBIJGUa06RJk/D27VusX78eQF5h77lz52L8+PEffsNERERERPRRSaXSahWX6FPHRFIVJpVKUa+xI+5fD0D4s0cfHM/c+gvUa+wIqVQq1ymcnTp1KrBz2/sMDAywcePGYvvUrl0ba9euLbZPcHBwgbbhw4dj+PDhwnNNTc0C66oLO7ewWEREREREVLVoaOmU3Kkc1DUrJi7Rp441kqqw/GRP+69HQk2jbAWn36emoYX2X4+QiUtERERERFTVGdWwrJi45hUTl+hTx0RSNaAjNsJXI74rdzJJTUMLX438DjpiIzmPjIiIiIiIqGLVsCpbqYvS0NY3hI6+odzjEn0OmEiqJowtrNB30gKYW9cv03nm1vXRd9J8GJvXrpiBlaBly5YIDg6GnZ1dpVyfiIiIiIiqNwMTc5jVrivXmLYt2so1HtHnhImkakRHbIReY2bjy37jYFrLpti+prVs8GW/ceg1ZjZnIhERERERUbXm0Lb0uziXRE1DCw2at5FbPKLPDYttVzP5BbjrNXZEQmwU3rwKRdybcGRnZUBJWRViE3OYWFhB18BY6M+aSEREREREVF1JpVLUltijXuNWeHz7ygfHa/PVIKhrasthZESfJyaSKkl5t5p8Nymka2AsJIxK058+Tdy2lIiIiIg+Zfl/07j2GIK4N68QE/Gy3LHsHN1Qr5H8d7Im+pxwadtHpqysDABITU2t5JHQpyIlJQUikUj43iIiIiIi+hSpqqmj5+jvYWZZvnpJjZ07wbXHECaRiD4QZyR9ZIqKitDT00NUVBQAQENDg/+JUZlJpVJkZ2cjMTERiYmJ0NPTg6KiYmUPi4iIiIioQqlpaKHXWA/cvuCHa/5HkJ2VWeI52noGaNd7BGrVbcgkEpEcMJFUCUxNTQFASCYRlZeioiLMzMygq6tb2UMhIiIiIvooRCIRmrq6o0FzVzy8eQFP715HTMRL5GRnCX3UNDRhUtMGXzg4w7phUygoKDKJRCQnTCRVApFIBDMzMxgbGyMrK6vkE4gKoaSkBEVFRf4yJCIiIqLPSv77X1V1DTRx6YwmLp2Rm5ODxPgY5GZnQ0VNHVq6YqF/fk1Rvm8mkg8mkiqRoqIilyMRERERERGVw7uJIZGCAvQMTErs96kLDAzE0KFDsW7dOnTu3LlcMYYMGQIA2LVrlzyHRp8QFtsmIiIiIiKiaq0qJ4vS09Pxww8/oFu3bnBwcECTJk3w1VdfYceOHeVeoTJixAhIJBIsXrxYzqMt6M2bN/D09MTDhw8r/FpUPTCRRESfrFevXkEikcDHx6eyh0IfKDAwEBKJBIGBgWU6z8PDA02aNKmgURERERGVLD09HU+fPkWbNm0wY8YMfP/996hfvz6WLVsGDw+PMsc7deoUbt++Lf+B/j9vb294e3sLz6OiouDl5cVEEgm4tI2Iqi0fHx/MmTMHf/zxB+zs7Cp7OBXqxo0bGDRoEADgypUrEIvFJZyRNy352rVrsLS0xKlTpwocv3TpEkaOHAkAHzT9+VN28eJFHD9+HHfu3EFISAjMzMxw9uzZAv08PT3h5eVVZJy9e/fCwcEBwH9fl3zKysowNjZG69atMXHiRJiZmZU4rqioKOzcuRP//vsv7t27h9TUVOzcuRMtW7aU6ZeWlgYfHx/4+/vj8ePHSElJgaWlJfr27Yt+/foVWF4dFRUFT09PXLp0CTExMTA2Nkb79u0xfvx46OvrC/3ev4d3WVlZwc/Pr9jx579epf1eLqtbt27h0qVLGDZsGHR0dOQev6LGIJFICm03NDTEpUuXABT8XlNTU4O5uTk6duyIMWPGQEtLSzj2/PlzrFu3Djdv3kRCQgLMzMzQrVs3jBo1Curq6h9wd0REVBZ6eno4ePCgTNuAAQOgra2N3bt3w8PDA0ZGRqWKlZGRgeXLl2P06NFYv359RQwXKioqFRKXPh1MJBERVXG5ublYunQpNDQ0kJqaWqZzVVVVERYWhjt37sDe3l7m2NGjR6GqqoqMjAx5DrdCNG/eHHfu3IGysvJHve7ff/+N48ePo0GDBjA2Ni6yX8eOHVGrVq0C7WvWrEFqamqBRKepqSlmzJgBAMjKykJISAj2798vJK5K+iM/NDQUW7duRe3atSGRSBAUFFRov5cvX2LJkiVo1aoVhg8fDi0tLVy8eBGLFi3Cv//+i59//lnom5KSgv79+yM1NRUDBw6EmZkZHj16hD179iAwMBA+Pj5QUPhvIvO79/AubW3tYsf+MQQFBcHLywu9evWqtERSecfQunVr9OjRQ6ZNTU2tQL+FCxcK/ydcunQJmzZtQmBgIPbt2weRSISIiAj06dMH2traGDx4MHR1dXH79m14enri/v372Lhx4wffIxFRcQIDA/Hzzz/jyZMnMDExwejRoxEdHQ0vLy8EBwcL/Y4cOYIdO3bg6dOnUFNTQ+vWrfH999/LfLAyZMgQvH37FitXrsTSpUtx9+5dGBsb47vvvkPnzp1x7do1/PLLLwgODoaZmRkWLFgAJycn4fz8JLyfnx82bNiAc+fOQVlZGf3798e3336LyMhILF68GIGBgVBXV8eoUaOED9sAIDMzExs3bkRAQADCwsKQk5ODBg0aYOrUqXB0dCz3a2Rubg4ASExMLHUiaevWrZBKpRg1alSxiaTc3FysXr0af/75J1JSUuDo6IgFCxaU6gOrd2sk5ddcAoA5c+b8H3v3HRbV8TVw/LusFFGKFFERu6IRsVdsEVRATBSxIhqjxt5NgokaTYw1iT2xBI0de8GOBewlkdg1ihXsotKk7r5/8LI/1wWFdY1ozud58sSdO3d27rLl3nNnzjBq1CgAJk2ahK+vb476LD48eSqQdPPmTYKCgjh9+jRXrlyhTJkybN26VavOlClTOHDgAHfu3EGhUFC6dGk+//xzWrVqpVUvJSWF6dOns2XLFhISEqhevTpjxoyhTJkyWvUiIyOZMGECERERFChQgE8//ZShQ4dKFFYIkWesXr2au3fv4ufnx9KlS3O1b4kSJUhLS2Pr1q1agaTk5GRCQ0Np2rQpu3btMnSXDSY5ORljY2OMjIwwNTX9159/2LBh/PDDDxgbG9OnTx+uXLmSZb2KFStSsWJFrbK7d+9y79492rdvr/ObYmFhoRMsKF68ON9//z2nTp3Czc3tlf2qXLkyx48fx9ramp07d2YbSLKzsyMkJITy5ctryjp16sSoUaPYsGED/fv3p2TJkgDs27eP6Oho5s+fT9OmTTX1raysmDt3LpcuXeKjjz565TG8j1QqFampqe/k/ZWVUqVK5eh1bdmypWY0V+fOnRk0aJBmqkP16tXZvHkzsbGxrFy5UvP379ixIyqVik2bNvHs2TOsrKze6rEIIf67Lly4QK9evbC3t2fQoEGoVCrmzp2rMwr1t99+Y+bMmXh5eeHn50dMTAzLly/H39+fTZs2aQXinz17Rt++ffH29sbT05NVq1YxfPhwVCoVEydOpFOnTvj4+BAUFMTgwYMJCwvTGqUJGb/rZcuWZcSIEYSHh/Pbb79hbW1NcHAw9erVY+TIkYSEhDBlyhSqVKlC7dq1AYiPj2ft2rX4+PjQvn17EhISWLduHb169WLt2rVUqlQpR69LSkoK8fHxJCcnc+7cORYtWoSjo6Pmt/h17ty5w8KFC5k4cWKWNxlefm0VCgW9e/fm8ePHLFmyhM8++4zNmze/dt8XlS1blsGDBzNr1iw6duyoGWFdo0aNHLchPjx5KkfSlStXCA8Pp2TJkpQtWzbLOgkJCbRv356ZM2cyc+ZMKlWqxPDhwwkJCdGqN2HCBNauXcuwYcOYPXs2KSkpfPbZZ8TFxWnqPHv2jO7du5Oamsrs2bMZNmwYa9asYfLkyW/1OIUQ786lS5cIDAzE3d2dKlWq4ObmxqhRo3jy5IlWvdmzZ+Ps7Mz169cZOXIkNWvWpF69esyYMQO1Ws3du3fp168fNWrUwM3NjUWLFmntn5KSwsyZM/H19aVmzZpUq1aNLl26cOzYsVz19+nTp8yYMYPBgwfrPbLCx8eH7du3o1KpNGX79u0jKSkpy+ls0dHRjBs3jpYtW+Lq6krdunUZPHgwUVFRmjpnz57F2dmZjRs36ux/8OBBnJ2d2b9/v6bs/v37jBo1igYNGuDi4kKrVq1Yt26d1n6ZeZC2bdvG9OnTadSoEVWrViU+Pj7bHEmnT5+md+/e1K5dm2rVqtG6dWuWLFnyytfj4sWL1KtXj4CAABISEl5Z18HBQe9RUFu3bkWtVtO6desc1bezswPI0WqeBQsWxNra+rX1bGxstIJImZo3bw5k3EzJFB8fD4Ctra1W3cw7pG870BIQEICPjw9Xr14lICCAqlWr0qhRIxYuXKhTd9myZbRq1YqqVatSu3ZtfH19NecBs2fPZurUqQC4u7vj7OyMs7Oz5v2bmZh0y5YttGrViipVqnDw4MFs32PZ5VqLjIxkyJAh1KtXD1dXV1q2bMn06dNz1Ie3IfOOeOZzvOrvaWRk9K+P7hNC/LfMmjULpVLJqlWr+OKLL+jbty/Lly/n6tWrmjrR0dHMnj2boUOHMn36dLp06cLAgQNZunQp9+/fZ+XKlVptPnjwgJEjR/LVV1/RtWtXZs+eTXp6OiNGjGDu3LkMHDiQ7t27M3HiROLi4rKc1u/q6srPP/9Mly5d+PXXXylSpAiTJ0/G19eXcePG0aVLF+bPn4+ZmRnr16/X7GdlZcW+ffsIDAykc+fO9OrVizVr1mBlZZWrlc1CQ0OpX78+TZs2ZeDAgTg4OPDbb7+RL1/OxndMnjyZSpUq6QyiyMqzZ89YuXIln332GSNGjGDKlCncuHFDZ4rd69jZ2dG4cWMAqlWrxqeffsqnn36Kk5NTrtoRH5Y8NSKpWbNmeHh4ABkJUs+dO6dT5+Ws9I0aNeLq1ats3LhRc7J+79491q1bx3fffYefnx8AVapU4eOPPyY4OJjevXsDEBwcTEJCAnPmzNGckKenpzN+/Hj69OmDg0PWy0cKId5fR44c4fbt2/j6+mJvb8+VK1dYs2YNV69eZc2aNTorfrzrO1czZ87E3t6eTp068euvv+p1zD4+PsyePZvjx49Tv359ICPIUa9ePZ2LTMgIEkVERNCqVSuKFClCdHQ0q1atolu3bmzbto38+fNTpUoVnJyc2LFjB23bttXaf/v27VhZWdGwYUMAHj16RIcOHVAoFPj7+2NjY8OBAwf49ttviY+P57PPPtPa/9dff8XY2JiePXuSkpKS7QXv4cOH6dOnD4ULF6Zbt27Y2dkRGRlJWFgY3bt3z3KfM2fO0KtXL1xcXPj1119zdUcut0JCQihatKjmPfGi9PR0YmJiAEhLSyMyMpLZs2dTsmTJf+UO36NHjwC08h7Vrl0bIyMjfvzxRwIDAylSpAiXL19m3rx5eHh46NzgefEYXmRmZoa5uble/Xr27Bm9evWiefPmeHl5sWvXLn766ScqVKhAkyZNAFizZg0TJkygZcuWdOvWjeTkZC5fvszp06dp3bo1zZs358aNG2zdupVRo0ZpjvHFu+DHjh1jx44d+Pv7U6hQIRwdHYmNjc1xPy9duoS/vz/58uWjY8eOODo6cuvWLfbt28ewYcNy1IfsJCcn67yuBQsWfO1I6Vu3bgFozmfq1KnDwoUL+fbbbxk8eDDW1tZERESwatUqAgIC9P4bCSHE66Snp3P06FE8PDy0rqdKlixJo0aNNDeaQkNDUalUeHl5aX3v2dnZUbJkSY4fP07fvn015ebm5loBlDJlymBpaYmDgwNVq1bVlGf++/bt2zp9y7w2hIwbNy4uLty7d0+r3NLSktKlS2vtr1QqNTd6VCoVsbGxqFQqXFxcuHDhQo5fm7p167J48WJiY2M5evQoly9f5vnz5zna99ixY+zevTvHgaA2bdpojcjy9PTE3t6e8PBwzVQ1IfSVpwJJL+ZeyA1ra2utu8qHDh1CpVJp3Wm3trbGzc2NAwcOaAJJBw4coH79+lp3db28vPjuu+84fPiwzPkU4gPUpUsXrTnvkHF3Zfjw4fz111/UqlVLa5urq6smgN2xY0eaNWvG5MmTGT58OF988QWQEahp1KgR69ev1wQNMu9cvXjx16FDB7y8vFi2bBkTJ058bV8vXbrE6tWrWbBgQY5GqWSnVKlSuLi4sHXrVurXr09sbCzh4eFMmDAhy/pNmzbVGan08ccf07FjR3bt2kWbNm0A8Pb2ZtGiRVpTZFJSUtizZw/NmzfXBICmT59Oeno6ISEhmgvqzp07M3z4cObMmUOnTp20AjrJycmsX7/+lUGe9PR0xo4dS+HChXWGvqvV6iz3+euvv/jiiy+oVasWs2fPfqtTmK9cucLly5fp1atXlssRX7t2TRPUy1S2bFmCgoLe+tTqlJQUlixZQvHixbVyN5UrV47vv/+eqVOn0rFjR01527Zts3yvZHUMkPE50Xcp4gcPHjBlyhTNe8zPz49mzZqxfv16TSApLCyM8uXLZ5sXomLFinz00Uds3boVDw8PihcvrlPn+vXrhISEUK5cOU1ZblYEnDBhAmq1mo0bN1KsWDFN+ciRI3Pch+ysW7dOZ7ReVnkonj17BqDJkbRy5Urs7Ow032GNGzdmyJAhzJ8/XytBfN++fRk2bFiO+yOEELn1+PFjkpKSspyu9WLZjRs3UKvVtGjRIst2Xh6lU6RIEZ3fVAsLC4oUKaJTBmR5g+DF7+zMuqampjqBfgsLC54+fapVtnHjRhYtWsT169dJTU3VlL/4HR8TE0N6errmsbm5OQUKFNA8trOz04xA9vT0ZN68efTo0YPdu3djb29PUlKS1gwayBhJmpaWxo8//sinn36qk/MyOy+//gqFgpIlSxIdHQ1kzPR5MfemUql8KwtgiA9Tngok5ZRarSY9PZ3ExET27dvH4cOHmTZtmmb7tWvXsLW11Zn7X7ZsWa2Ts2vXrtGuXTutOpaWltjb23Pt2rW3exBCiHfi5YBFQkKC5s7V+fPndQJJ7/LO1Y8//kjjxo01I3veROvWrfn111/57rvv2LVrF0qlEg8PD86fP69T98XXKDU1lfj4eEqUKIGlpSUXLlzQCiTNnz+f3bt30759eyBjlFBsbCze3t5Axvf17t278fLyQq1Wa91xbNiwIdu2beP8+fOa+faQcQftdSOFLly4QFRUFKNGjdKZ8pdV4ObYsWP069cPNzc3fvnll7cerMmcZpXdtDZHR0dNcCYtLY3r16/z+++/07t3b1auXPlWT+R++OEHrl69yoIFC3RO0h0cHHB1daVx48Y4Ojry559/smzZMgoVKsTXX3+d7TG83Ia+zM3NtfIDmZiYUKVKFa3PlqWlJffu3csygXxO1a5dWyuIlBsxMTGcPHmSbt266VyQZPXeyy13d3e6du2qVZZVX18O9pYvX57JkydrJWp3dHSkVq1atGzZEmtra8LCwpg/fz729vY6zyGEEP82lUqFQqFg4cKFGBkZ6XyHvjxyMrubatmVZ3VjKauBCznZf/PmzQQGBuLh4UHPnj2xtbVFqVQyf/58rd8oPz8/TaAGYODAgQwaNCjL9gHNtOi9e/fSqVMntm/frklmneny5cts2rSJ69evM378eJ1p0gkJCURFRaFSqXI1MGPRokVaq4A6OjpmuTKtEFl5LwNJR48epUePHkBGpHrMmDFaJ1SxsbFZrhpjaWmpuYOXWS+rnCNWVlZa9YQQH46nT58yZ84ctm/fzuPHj7W2vXwHCN7dnavt27cTERGhk//tZTm9m+Tt7a1ZrGDLli00bdpUJwFlpqSkJObPn8+GDRu4f/++1onUi69RxYoVKVOmDDt27NAEkrZv306hQoU0+VpiYmKIjY1l9erVrF69Osvne3kaT05Gb2SetFWoUOG1dZOTk+nTpw+VK1dmxowZOsGTuLg4kpKSNI+NjY1zlH8oO2q1mq1bt1KhQgWdBNyZzM3NtVaTady4MTVr1qRdu3YsWLCAwMBAUlJSdH6LbGxs3mh02u+//86aNWsYMmSIZoRPpr/++ou+ffuyevVqzUglDw8PChYsyJw5c2jXrp1WQOPlY3iZPv3P6m6zlZWV1uo+vXv35siRI7Rv356SJUvi5uaGj4+PVjDydXIzQuhluXnvZeXp06da3wdmZmZa5yxFihR55euaafbs2RQsWJB8+fJRpEgRnVUDt23bxtixY9m1a5fmbn2LFi1Qq9X89NNPtGrVSmtqoxBCGIqtra1m1diXvVhWokQJ1Go1xYsXp1TJEiTfjST5biRpT++jVqlQqixJvJqOaTH9vm8NadeuXTg5OTFnzhyt36mXR8dOmzZNazXc1+URyqybeX7VsGFDFi9erFPv7t27pKam0rlzZ51tmzZtYtOmTdja2mrdTHj59Ver1dy8eRNnZ2cg48bdi7+dr8qFaIgbJeLD8l4GklxdXVm3bh3x8fEcOHCACRMmoFQqNRcyQgjxMrVajUKhYOjQoURERNDj8x6Ur1AeSwtLUEOvXr1Qq9Waepne1Z2rqVOn0rJlS4yNjTV3njKHaN+7d4/U1FQcHBxyfDepcOHC1KlTh8WLF3Pq1Clmz56d7Wv1ww8/sGHDBrp37061atWwsLBAoVAwbNgwnbt73t7ezJs3j5iYGAoWLMi+ffto1aqVJliTmeD7k08+0cmllCnzhCaTofMWmZiY0LhxY/bt28fBgwf5+OOPtbb/+OOPWknD69Spk6vEmS/766+/iI6OZsSIEbnaz8XFBQsLC06ePAlkLB//cg6DvXv36h0E2bBhAz/99BOdOnWif//+OttXr16Nra2t1nQ3yMhfOHv2bCIiInI1ikef/uckSFa2bFl27txJWFgYBw8eZPfu3axcuZIBAwYwePDgHPUtq/dYdifJLyapN4RBgwZx4sQJzeO2bdvqtchHrVq1XjlybeXKlVSqVElnykezZs3YsGEDFy9ezFHASgghckupVNKgQQP27t3L/fv3NSNVb968ycGDBwE0U9p++eUXfhn7JSNqWaOK/9/CJ2q1mrjkdCzN8oFRPlIe3kelNtZs+7cDG5m/Ty8+9+nTp/n777+1bjpmd1MjJiaGQoUK6fR77dq1QMY5AGScrxUuXFhnf29v7yzzaw4YMIAmTZrQoUMHnVHCmzZt4osvvtDcONy5cycPHz7UpHlxcnLKccLszABVbvIJig/bexlIKliwoOZEt379+qSnp2uy7SuVSiwtLTWrlbwoNjZWa7qbpaVlliMQZElcIT48atQcuHSUo0ePUq6lC0eL/MOR2Mso44ywSsqYu/4wIcZgJyZveufq7t27bN26la1bt+q03bZtWypWrMjmzZtzdTfJx8eH0aNHY2lpqVl9I7u+t2nThsDAQE1ZcnJylt+X3t7ezJkzh927d2NnZ0d8fLxWIkwbGxsKFCiASqUy6EVr5uv0zz//vLZdhULBTz/9RP/+/RkyZAgLFy6kbt26mu29evXik08+0TzWd3W8TCEhISgUCnx8fHK9b+a0bcgY8fXyXcnMFdRya8+ePYwePZoWLVrw3XffZVnn8ePHWQZN0tLStP6fU4bs/8vMzc3x9vbG29ublJQUBg0axLx58+jTpw+mpqZ6fY4z/+4vv89fDPSC9nvvVbLrw9dff611Ip7VBYMhPHr0KMtzmczRULn9ewohRG4MHDiQQ4cO0blzZzp37oxKpWL58uWUL1+eixcvolAosE1+QPfaTiw6dpao6+bUd7Igv7GSe/EpHL0Vi2cFG/wq24EqjfTEZ6Qlp/M49A8KNe2CwvjtTlF/WdOmTdm9ezcDBgygadOmREVFERwcTLly5bRGhmdny5YtBAcH4+HhgZOTEwkJCRw6dIjDhw/z8ccfZ5lz8EVly5bNdlXz4sWL4+HhwaRJk7TKrays6NKlC76+vjx+/JglS5ZQsmRJOnTokPMD/3+ZKQ6Cg4MpUKAA5ubmuLq6yspt/2HvZSDpZZUrV2bJkiXExMRgb29PmTJlePTokU5A6Nq1a5QpU0bzuEyZMjq5kOLi4nj48KFWPSHE++3G0ygGbRvLvcf3AYhLSaAAGSNr0tUqzoT+CUDY9SN8vXsSX9Tq8sbP+aZ3rubOnatTtm3bNrZv386UKVM0owxyczfJ09OTe/fuUbp06VfmCMpqVMiyZcu0puBlKlu2LBUqVGD79u3Y2dlhb2+vtUqZUqmkZcuWhISE0KdPH53pQDExMXrlA6pcuTLFixdn6dKl+Pr66iTbfvki3sTEhDlz5tCzZ0/69u3LkiVLNPl1ypUrp3e+nJelpqayc+dOatasqTMt8nWOHTtGYmKiZjqclZWVQYJvJ0+eZPjw4dSqVYuffvop2/wJpUqV4tChQxw/flwr0JYZzPzoo49y9byG6v/Lnjx5ojUly8TEhLJly3LgwAFSU1MxNTXV3DnNKviZHUdHR5RKJSdPntSsIAuwatUqrXo2NjbUrl2b9evX06NHD62/84vvvez6kHnX+W0rXbo0hw4d4vr165QuXVpTvm3bNoyMjHRGAgohhCG5uLiwcOFCpk6dysyZMylatCiDBw8mMjKSa9eu8fTYFmL2LqF9hYIUM3Vi48XHrDjzEAD7AsbUKFaQek66qUqenQgh6c4/FO00BiPT/Drb3xZfX18ePXrE6tWrOXToEOXKlWPatGns3LlTa5RpdmrWrElERATbtm3j0aNH5MuXj9KlSzNq1Ki3lrOub9++XL58mQULFpCQkED9+vX57rvvtKa/5ZSxsTGTJ0/ml19+Ydy4caSlpTFp0iQJJP2HfRCBpL/++ouCBQtqTiwbNmyIkZGRVgLYZ8+ecejQIa3h/I0bN2bevHlauZJ27tyJkZERbm5u//6BCCH0sn79es1Q6Rd93CxjCtP2f/ZhU6AoSrN8FChpzcNDN1GnqzC2NCUuMoaUJ//Lj3P9yS2+3TOVQg/0z0MDb37n6sUL2UwXL14EMr679Am+WFhYvDLh44t937x5MwULFqRcuXL8/fffHDlyJNu8Qd7e3syaNQtTU1P8/Px0AhUjRozg+PHjdOjQgfbt21OuXDmePXvG+fPnOXr0aI5OwF5mZGTEuHHj6NevH23atMHX11ezUMLVq1cJCgrS2cfMzIz58+fTrVs3evfuzbJly16b5+bSpUuaqYI3b94kLi6OX3/9FcgYcdOsWTOt+ocOHeLp06fZJtnOFBcXx+bNm4GMUUjXr19n1apVmJmZaVYDfJ3Mfly9ehXImE75119/AWh+66Kjo+nXrx8KhYKWLVuyY8cOrTacnZ01gSt/f382bNhA3759CQgIoFixYpw8eZKtW7fi5uamtbTyy8fwshcTZhtaz549sbOzo0aNGtja2nLt2jWWL19OkyZNNMP3K1euDGSsGOjt7Y2xsTEff/zxK5e8t7CwwNPTk+XLl6NQKHByciIsLEwnlxrA6NGj6dy5M23btqVjx44UL16c6OhowsLCNK+JPn0wpJ49e3LgwAH8/f3x9/fXJNs+cOAA7du3f6Ok6EIIkRP169fXmjoOGb9PhQtZErN3iabMraQVbiVfPRtkasv/3eRPjrrMvbWTKer/HXv37s1yBOiLufUgY1pxVudAkydPznJ68ctT3BUKBX369KFPnz5a5U2bNn1lvzNVqVKFmTNn5qhubrx8nAB169bVlLdq1Yrhw4fnut2spvi7u7vj7u6e+06KD1KeCiQ9f/6c8PBwIOPkNz4+np07dwIZOSsePHjATz/9hKenJ46OjiQmJhIWFsbatWsZPny4JidHkSJF8PPzY+rUqRgZGeHg4MD8+fOxsLCgU6dOmufr1KkTy5YtY8CAAfTp04f79+8zdepUOnXqJCdYQrxHXh4xkCky/12dspLtKxO97R8en4hGDViUtaFMQFUuTDusqaNSqzj/4KrmsT5z8d/0ztW79O2332JkZERISAjJycnUqFGDxYsX06tXryzre3t7M2PGDJ4/f46Xl5fOdjs7O9auXcvcuXMJDQ1l1apVWFtbU65cOc1y6fpo1KgRS5YsYe7cuSxatAi1Wo2Tk9Mrh2wXLFiQoKAgunbtyueff86KFSuyXJ4404ULF3RO/DIft23bVieQFBISgrGxsc6KWi+7d+8eX331FZBxcmplZUXt2rUZOHBgljkQsvJyv9avX6/5d2YgKSoqSjMi5vvvv9dpY+DAgZpAUpkyZVi/fj0zZsxgy5YtPHr0iMKFC/P5559nmXvoxWN42dsMJHXs2JGQkBAWL15MYmIiRYoUISAgQOtGkaurK0OGDCE4OJiDBw+iUqnYu3fva4M4o0ePJi0tjeDgYExMTPD09OSrr77SmaZYsWJF1qxZw8yZM1m1ahXJyckUK1ZM6/2vbx8MpXbt2gQHBzN79mxWrVrF06dPcXR0ZNiwYdl+loUQwpCSkpK0ctLduHGD8PBwPMpav3nbN8/x7MQ2rOu++saNEOLtUKizWhfxHYmKiso2yrl06VLKli3LxIkT+fvvv3n48CEWFhaUKVOGzz77TOfufUpKCtOnT2fz5s0kJCRQo0YNRo8erTO3NDIykh9++IGIiAgKFCjAp59+yrBhw/ReGjqz/3v37tVrfyGEYaw9t42153XzC+XWsAa9qO+U89WghBBCCCFExiyRtm3b4uTkRHR0NMHBwSQnxjPbuzSOltnndMwpRT4TSgyaj9L8zXIbfijkOlT8m/JUIOlDIB9gId69W0+j+Tp0Eukq3Zw+uWVpasF0r++wMC1ggJ4JIYQQQvw3jBo1iuPHj/Pw4UNMTEyoWqUynWwfU87GcKuzFmrqTyE3X4O19z6T61Dxb8pTU9uEEMIQNl7cZZAgEkBschy7rx6gXWXdKVtCCCGEECJrL68ilplg25Dizx+QQJIQ70DWS7cIIcR7KjYpjuNREQZtc8+1g6jUKmQApxBCCCGEfpLvXn19pVxKfRSNKiVJztGE+JdJIEkI8UG5+PAqaao0g7b5OPEJd+Lu5zrhthBCCCGEyJDy8JbhG1WrSHkUJedoQvzLJJAkhPigXHvyFk5SgOsxb6ddIYQQQoj/AnVqyltqN/mttCuEyJ4EkoQQH5SY50/fq3aFEEIIIf4LFMZvvlJbVoxM3k67QojsSSBJCCGEEEIIIcRbZWLvZPhGFUYY2xaXHElC/MskkCSE+KAUym/1ltq1fivtCiGEEEL8F5gVK2/wNo3timNkYiY5koT4l0kgSQjxQSlTqMR71a4QQgghxH9BgY/cwEhp0DYtXBobtD0hRM5IIEkI8UH5yL48ypdOUlKePOf02H3ERNzVq02b/NYUs3SQYdNCvANRUVE4OzuzYcOGd90VIYQQbyCfhQ0FnOsarD2FsSkW1dwN1p4QIufyvesOCCHEm9qwYQOjRo1i3bp1VKlShbqO1Tly+0+Dte9RtiFGincTd09KSuL777/nzJkz3L17F5VKhZOTE+3ataNLly4YGxu/cv/79+8zbdo0zp49y4MHD1AqlZQqVQp/f3/atGmTo6Hgzs7O2W5r0KABixcv1jxWqVQEBQWxatUqHj58SKlSpejTpw8+Pj5a+wUEBHDixAnNY2NjYwoXLoybmxv9+/enaNGir+3X7NmzmTNnDkePHsXGxua19XPr1KlTHD58mO7du2NpaWnw9t+XPrxtL39+P2R//vkn/v7+ADl+36pUKrZs2cKKFSu4efMmqampFC5cmKpVq9KlSxeqVav2lnv9auHh4Zw5c4ZBgwbleJ+VK1dy7Ngxzfda27ZtmTx5sk69l78nXpQvXz7Onz+vefzy91T+/PkpVqwYPj4+9OjRg/z587+2X2fOnGHDhg2cOXOGy5cvk5aWxuXLl7Ot/+jRI2bNmsX+/ft5+vQp9vb21KtXj4kTJ2rqBAYGsnHjxiz3NzEx4ezZs6/s09v+fFy9epUdO3bQtm1bihcvbvD235c+iH+PrUd3Eq/9jTo58Y3bsmnqj9L8w/xtFCKvk0CSEOKD0+ajlhyPOkW6WvXGbVmYFKBFuXc3bDopKYmrV6/SuHFjHB0dMTIyIiIigkmTJnHmzBl+/vnnV+7/5MkT7t+/j6enJ0WLFiUtLY3Dhw8TGBjI9evXGT58+Gv7MHXqVJ2yc+fOsXTpUtzc3LTKp0+fzoIFC+jQoQNVqlRh7969jBgxAoVCQatWrbTqFilSRPP8qampREZGEhwczKFDh9i+fXuOLvzepoiICObMmUPbtm3fWRAnL/RBGIZKpWLChAmYm5uTmJjzC6gJEyawYsUK3N3dad26NUqlkuvXr3Pw4EGcnJzyRCBpxYoVuQok/f777yQkJFClShUePnyYbb2+ffvi5+enVfb8+XO+++47ne8eADc3Nz799FMAEhMT+fPPP5k5cyaXLl1i1qxZOTqWdevWUaFCBYoXL86NGzeyrXv37l06d+4MQKdOnXBwcODBgwecOXNGp66JiQkTJkzQKVcqDTvFRx9Xr15lzpw51KlT550Gkt51H8S/J5+lHXYte/Fwy+s/k6+Sv7QrVnVaoVarJT+SEO+ABJKEEB+cUtbFaVPJk/UXtr9xW5/X7ISlqYUBeqUfa2tr1qxZo1XWuXNnLCwsWL58OYGBgdjb22e7f8WKFVm2bJlWWdeuXenbty/Lli1jyJAhr72Yybwwe9GJEydQKBRaI43u37/P4sWL8ff3Z+zYsQC0b9+erl27MnXqVDw9PbWey8LCQqft4sWL8/3333Pq1KksLxTzKpVKRWpqKqamsgSxyNrq1au5e/cufn5+LF26NEf7PHr0iJUrV9KhQwd++OEHrW1qtZqYmJi30dW3btmyZRQrVgyFQkH16tWzrZfVd8DmzZsBaN26tc62UqVKaX2ndO7cmdTUVEJDQ0lOTn7t57Nz58707t0bMzMzvv/++1cGksaOHYtSqWTdunUUKlTole3my5cvy+/R941arSY5ORkzM7N33RXxHlOr1VhUaYLqeTyPQxcDuU8bYOb0EQ5+XwFIEEmId0SvuRqbNm167X87duwgIiKClJQUQ/dZCCFeSa1W41fZm5rFXLOt8/xePLc2XODi9COc+T6M81MPcWvjRdISUzV1fJzdObX5KM7Ozly/fp2RI0dSs2ZN6tWrx4wZM1Cr1dy9e5d+/fpRo0YN3NzcWLRokdbzpKSkMHPmTHx9falZsybVqlWjS5cuHDt27I2O0dHREYDY2Fi993/+/Dmpqamvr/ySlJQUdu/eTe3atSlSpIimfM+ePaSmptKlSxdNmUKhoHPnzty7d4+IiIjXtm1nZwfof6c+ICAAHx8frl69SkBAAFWrVqVRo0YsXLhQp+6yZcto1aoVVatWpXbt2vj6+hISEgJkTJvLHInl7u6Os7Mzzs7OREVFARnTaL7//nu2bNlCq1atqFKlCgcPHuT48eM4Oztz/PhxrefKLs9PZGQkQ4YMoV69eri6utKyZUumT5+eoz78l126dInAwEDc3d2pUqUKbm5ujBo1iidPnmjVmz17dp74/D59+pQZM2YwePDgXI0si4qKQq1WU6NGDZ1tCoUCW1tbrbLY2FgmTpxIs2bNcHFxoXHjxnz11VdaAafHjx/zzTff0KBBA6pUqcInn3yiM/Uqp+/jwMBAVqxYAaB5f75qKmwmR0dHvS/+tm7dirm5Oe7uOcuLYm9vj0KhyNF3ip2dXY6CJJGRkRw4cICePXtSqFAhkpOT9fou1VdgYCDVq1fn/v379O/fn+rVq1OvXj2mTJlCenq6Vt1t27bh6+tL9erVqVGjBq1bt2bJkiVAxrS5IUOGANCtWzfN3y/z796sWTP69OnDwYMH8fX1xdXVleDg4FfmLXN2dmb27NlaZffv3+ebb76hYcOGuLi40KxZM7777jtSUlJe2wfx4VEoFKjVaqzqtKJIp29RWti+fifNzkZY1W9D0S5jMTJ5t6OWhfiv02tEUmBgoOYE4OXksy+WKxQKChYsyBdffEHv3r3fsKtCCJEzCoUCpULJCLfezD+5gtAn+3XqxEXGkPLkOTbVi5KvoAlJDxJ4/Ncdkh8kUKFPbfwqt6K9SytmHcgYej1s2DDKli3LiBEjCA8P57fffsPa2prg4GDq1avHyJEjCQkJYcqUKVSpUoXatWsDEB8fz9q1a/Hx8aF9+/YkJCSwbt06evXqxdq1a6lUqVKOjiklJYX4+HiSk5M5d+4cixYtwtHRkZIlS+Zo/6SkJBITE0lMTOTkyZNs2LCBatWq6XVnOTw8nNjYWD755BOt8osXL2Jubk7ZsmW1yl1dXTXba9WqpSlPT0/XXOCmpaURGRnJ7NmzKVmyZJYXzjn17NkzevXqRfPmzfHy8mLXrl389NNPVKhQgSZNmgCwZs0aJkyYQMuWLenWrRvJyclcvnyZ06dP07p1a5o3b86NGzfYunUro0aN0ow4eDGnzbFjx9ixYwf+/v4UKlQIR0fHXAX2Ll26hL+/P/ny5aNjx444Ojpy69Yt9u3bx7Bhw3LUh/+qI0eOcPv2bXx9fbG3t+fKlSusWbOGq1evsmbNGp0gxbv+/M6cORN7e3s6derEr7/+muPjLFasGAA7d+7E09PzldM9ExIS8Pf3JzIyknbt2vHRRx/x5MkT9u3bx/3797GxsSEpKYmAgABu3bqFv78/xYsXZ+fOnQQGBhIbG0v37t1z3DeAjh078uDBAw4fPpzlFFhDi4mJ4ciRI3h5eWFubq6zPTk5WfOd8vz5c06dOsXGjRvx8fEhXz7DDcI/evQokBF46t69O8eOHUOpVNKgQQPGjRuX5fSsrEaPmZiYULBgQb36kJ6eTs+ePXF1deWrr77i6NGjLFq0CCcnJ00w//DhwwwfPpz69eszcuRIAK5du8apU6fo3r07tWvXJiAggGXLltG3b1/KlCkDoPUdfv36dUaMGEHHjh3p0KEDpUuXzlU/79+/j5+fH3FxcXTo0IEyZcpw//59du3aRVJSUo76ID48mcEk87LVceozg2d/7iD21G7SYx9lXV9pTIFK9bGq2xrTImVk8RMh8gC9flU3bdpEYGAg1tbW+Pv7U6JExrLYN2/eZMWKFcTFxTFmzBgeP37MsmXL+OWXXyhQoIDWXWohhHjb8hnlY0Dd7pRWFmUYR7W22dVxpLBbCa0ycycrbq09Tyc7L9q6aM+7d3V15fvvvwcyLp6aNWvG5MmTGT58OF988QUAPj4+NGrUiPXr12suRK2srNi3bx8mJiaa5+nQoQNeXl4sW7ZMKynrq4SGhmrlM3JxcWHixIk5vjhaunSpVj6l+vXrM2nSpBzt+7KQkBBMTExo2bKlVvnDhw+xtbXVuYjPnHr34MEDrfJr165Rv359rbKyZcsSFBSk9Xrl1oMHD5gyZQpt2rQBwM/Pj2bNmrF+/XpNICksLIzy5ctnmzelYsWKfPTRR2zduhUPD48sLwyvX79OSEgI5cqV05Tl5i76hAkTUKvVbNy4URMwADQXfDnpw39Vly5d+Pzzz7XKqlWrxvDhw/nrr7+0Apbwbj+/ly5dYvXq1SxYsCDXI+0KFy5MmzZt2LRpE02aNKFOnTrUqFGDJk2a6FxoBwUF8c8//zBnzhyaN2+uKe/fv7/momv16tVERkYybdo0TSC4U6dOBAQEMGPGDNq1a5erwEb16tUpVaoUhw8f/lembm3fvp20tLQsp7UBrFu3jnXr1mmVeXh4ZJmf6E1kTnkbM2YMVapUYfr06dy9e5c5c+bQo0cPtmzZohX0S0xM1PmuA2jYsCFBQUF69SE5ORkvLy8GDBgAZEzLa9u2LevWrdOcb4eFhVGwYEGCgoKyfO85OTlRq1Ytli1bRoMGDahbV3c1rZs3b/L777/TqFEjTVluRkX+8ssvPHr0iDVr1mglCx8yZAhqtRpLS8vX9kF8mDLPFYxMzSnk1g7r+m1JeXCD5LuRpD65D2oVyvwWmBQpjWmx8ijNCujsK4R4d/QKJC1ZsgRbW1udHz9nZ2eaN29Or169WLduHT/++CPNmjWjS5curFq1SgJJQoh/nVqtxrXIRwB4lmuC0qkgkTG3eJCQcddLoVDgYGZHyQLFqO73Ef3W9iY2KkazLdOLCV+VSiUuLi7cu3dPq9zS0pLSpUtz+/ZtrbqZJ/AqlYrY2FhUKhUuLi5cuHAhx8dRt25dFi9eTGxsLEePHuXy5cs8f/48x/u3atUKFxcXYmJi2L9/P48fPyYpKSnH+2eKj48nLCyMJk2a6EzRSUpKyjIAlJmX5OXnc3R01FzgpaWlcf36dX7//Xd69+7NypUr9R55Y25urnVRa2JiQpUqVbT+LpaWlty7d48zZ85oRkzlVu3atbWCSLkRExPDyZMn6datm1YQCeQEOSdeHEmXnJxMQkICVatWBeD8+fM6gaR3+fn98ccfady4MQ0bNsz9gQKTJk3C1dWV9evXExoaSmhoKFOmTKFevXpMnToVBwcHAHbv3k3FihW1gkiZMt9TBw4cwN7eXiu3mbGxMQEBAQwfPpyTJ0/y8ccf69XPf8PWrVuxsbHJNn+au7s7Xbt2BTJGJJ0+fZo//viDESNGMGvWLIN9tjKTpdvb27NgwQKMjDIyRWQuILB161bat2+vqW9qasq8efN02nldbqXXyUz2nalmzZps2bJF89jS0pLnz59z+PBhGjfWb9GI4sWLawWRckOlUrFnzx4+/vjjLFeck+86oUWhwLRIGUyLlNHZJCOQhMh79Aok7dmzh2HDhmW5TaFQ0KxZM2bOnMmPP/6IkZGRVs4HIYT4N714olrGpiS+DXwBePjoIXN/ncvunbv5+/FjrX3i4uJ02nn5Yt/CwgJTU1OdYIeFhQVPnz7VKtu4cSOLFi3i+vXrWnk0XhxhEhMTo5XbwtzcnAIF/nf3zc7OTpM/yNPTk3nz5tGjRw927979ymTbmRwdHTV5lXx8fBgzZgw9evRg586dmJmZkZCQoLWSlFKpzDKQs2vXLpKTk7McEWBmZpZlXrzk5GTN9heZm5vToEEDzePGjRtTs2ZN2rVrx4IFCwgMDCQlJYVnz55p7WdjY/PKkR1FihTRuUCxsrLSWsa7d+/eHDlyhPbt21OyZEnc3Nzw8fGhZs2a2bb7sjcZIZQZrKhQoYLebfyXPX36lDlz5rB9+3Ye5+HP7/bt24mIiNDk3srOqz5/RkZG+Pv74+/vz5MnTzh16hTBwcEcOHCAYcOGsXLlSgBu3bpFixYtXvk80dHRlCxZUhP4yJQ5uunOnTuv3D83Xvedllu3b98mIiKCrl27ZjsSs0iRIlrfKe7u7lhbWzNlyhT2799Ps2bNcvxd9yqZ32Wenp5ar6WnpydfffUVp06d0gokZU57y86L03wzWVlZvXJkZlbvXysrK63vyy5durBjxw569+6Ng4MDbm5ueHl55Sqo9CbfczExMcTHx1O+fHm92xD/Ha8KLErQUYi8R69Akkql4vr169luv379OirV/5bdNjExkZVshBB5hlqt5suRXxIREUHPnj2pVKkS5ubmqFQqevXqpfX9lenlCy/IPiH0i3fONm/eTGBgIB4eHvTs2RNbW1uUSiXz58/XGvng5+dHdHS05vHAgQNfuZx2ZoB+7969dOrUKUfH/fL+a9as4eTJkzRq1IhFixYxZ84czXZHR0f27duns19ISAgWFhZZjlqwt7fn+PHjOkvxZi7vXbhw4df2y8XFBQsLC06ePAlAREQE3bp106qzd+/eV17c5GT6UNmyZdm5cydhYWEcPHiQ3bt3s3LlSgYMGMDgwYNfuz/oBsYg+5PdrN5TQj9qtZqhQ4dm+/nN6s71u/r8Tp06lZYtW2JsbKyZDpSZR+vevXukpqbi4OCQ489foUKFcHd3x93dnYCAAE6cOEF0dLQmSGwohngf5/Y77XUyg3HZTWvLTuaUspMnT9KsWbMcv9avkvldlhncz6RUKrG2ts71Igh3797VSR6+dOnSV07zysn3nK2tLZs2beLQoUMcOHCAAwcOsGHDBtq0acOUKVNy1LfcfM+9nOhbCCHEh0uvQFKzZs1YtWoVJUuWpH379pogUXJyMmvWrCE4OBgvLy9N/YiICE0eJSGEeNcePXnE0aNHadzenVhXCE/7G5MUY8wTM+7+pqrSDPZcu3btwsnJiTlz5midfL+cm2fatGmakTuQkbviVTLrZjX6Iicyp5ll7t+mTRut0ThZBf8fPHjA8ePHadu2bZZ3yitVqsTatWuJjIzUmvJ1+vRpzfacSE9P14wYqFixIosXL9banpMRWDlhbm6Ot7c33t7epKSkMGjQIObNm0efPn0wNTXV6w5o5nS/l/8uL15Qw//+vv/8888r25O7sP+TGeDJnN7ZrGNLkqobcyDlb4ySjTCJywgWJaQkvqqZXHnTz+/du3fZunUrW7du1Wm7bdu2VKxYkc2bN+fo8/cyFxcXTpw4wcOHD3F0dKREiRJcuXLllfs4Ojpy+fJlVCqVVnDt2rVrwP9GbuX0fQzZv0dz+532Olu3bqVEiRJUq1YtV/ulpWV8n2d+p+jzWr+scuXKQEYi6RelpKTw5MmTXI9wsre31/meq1ixYq77lRUTExOaNWtGs2bNUKlUjBs3jtWrV9O/f39Kliyp13eMlZUVoLtq6Msj2mxsbChYsOBr35fyPSeEEO8fvQJJ3377Lbdv32bChAlMmTJFc1L/8OFDUlNTcXV15dtvvwUyLnbMzMzo0aOH4XothBC5kHkBmpKeyvLTG9h1IQyAy4+u8ez+/0YfRG3NmPq09fIeCp50oFOVT3Tayq3Mu8YvjtI5ffo0f//9t9Z0m+ymVMXExFCoUCGdE+21a9cCGReTmeLi4njw4AGFCxfGwsJCs39WFzXr1q1DoVBoLoicnJxee6G3fft2VCpVtiMC3N3dmTRpEitXrmTs2LGa4w4ODsbBwYHq1au/sn3IWAktMTFRcxFlZWX1yikh+nry5IlWfhITExPKli3LgQMHSE1NxdTUVJMsNzfBOkdHR5RKJSdPnsTDw0NTvmrVKq16NjY21K5dm/Xr19OjRw+t98KL7xV9+vChUigUhF49wIbTOwA4/+AfHt/531SzzM/v9iv7UR8oQKcqb578+U0/v3PnztUp27ZtG9u3b2fKlCkUKVIEyP7z9/DhQ549e6aTiyslJYWjR49iZGSkuVHXokUL5s6dS2hoqE6epMz+N27cmEOHDrF9+3ZNnqS0tDSWLVuGubm5Jsl4Tt/H8L/3aGxsrFbetNxME32dCxcuEBkZSf/+/XO97/79Gat2Zn6n5OS77nXq1q2Lra0tISEh9O3bVxOM2rhxI+np6bn+zjI1Nf1XvueMjIxwdnYG0ExD1uc7pmDBghQqVIg///yTzz77TFOeOc3yxefz8PBgy5YtnD17VidPUub7Ur7nhBDi/aNXIMna2ppVq1YRGhrKoUOHNHeoGjZsSMOGDfHw8NDc6TI1NTX4ahlCCJGV9evXc/DgQZ3yzCkDq89uwVhlCUZQoKQ1Dw/dRJ2uwtjSlLjIGFKeZIzSSVels/faIU5ERWD3TP/VwwCaNm3K7t27GTBgAE2bNiUqKorg4GDKlSunlacjO1u2bCE4OBgPDw+cnJxISEjg0KFDHD58mI8//lhrJaDQ0FBGjRrFpEmT8PXNyAX122+/cerUKRo1akSxYsV4+vQpu3fv5uzZswQEBFCyZMkcH8uWLVsoXLhwttMtihQpQrdu3QgKCiItLY0qVaqwZ88e/vzzT3766SedqRhxcXFs3rwZyBiFdP36dVatWoWZmZlmJa23pWfPntjZ2VGjRg1sbW25du0ay5cvp0mTJppVqzKDbNOnT8fb2xtjY2M+/vjjLJcdz2RhYYGnpyfLly9HoVDg5OREWFiYTh4fgNGjR2tWWurYsSPFixcnOjqasLAwzeuiTx/eV6/7/G68uIvk1Ixzi1d9flGribh7njP3LmL1QHdKW2686ef3xSBMposXLwIZOcFeN3Ll3r17tG/fnnr16lG/fn3s7Ox4/Pgx27Zt49KlS3Tv3l3TRs+ePdm1axdDhgyhXbt2VK5cmWfPnrFv3z7Gjx9PxYoV6dixI6tXryYwMJDz58/j6OjIrl27OHXqFN98843mvZ+b93Hme3TChAk0bNgQpVJJq1atXnlc+/bt49KlSwCkpqZy+fJlfv31VyBj1PvLo3FyOq3txo0bms9OUlISf//9N5s2baJkyZI5WlUuOjpas/+5c+cANP0qVqyYZiVIExMTvvrqK77++mv8/f359NNPuXPnDsuWLaNWrVo6uarS0tI07b6sefPmb+3zPHr0aJ49e0a9evVwcHDgzp07LF++nEqVKmnyYlWqVAmlUsnChQuJi4vDxMSEevXqYWtr+8q227dvz4IFC/j2229xcXHhzz//zDLtxfDhwzl8+DABAQF06NCBsmXL8vDhQ3bu3MnKlSuxtLTUuw9CCCHeHb0CSZBxZ7BFixavTewohBD/lqzulgNYlck4GY1LScCGjDvmJdtXJnrbPzw+EY0asChrQ5mAqlyYdlizX1xKAldun9M8fjn3T074+vry6NEjVq9ezaFDhyhXrhzTpk1j586dnDhx4rX716xZk4iICLZt28ajR4/Ily8fpUuXZtSoUZrViV6ladOm3L59m/Xr1/PkyRNMTExwdnZm0qRJtG3bNsfHce3aNc6fP0+PHj2yzDeTaeTIkVhZWbF69Wo2bNhAqVKlmDZtWpYXgPfu3eOrr74CMn5TrKysqF27NgMHDszxNDh9dezYkZCQEBYvXkxiYiJFihQhICBAa8SDq6srQ4YMITg4mIMHD6JSqdi7d+9rL/pGjx5NWloawcHBmJiYaBLwvrhSFmSMkFizZg0zZ85k1apVJCcnU6xYMa2p4fr24X2U3ee3bJWMhOR34+5jQ1EgZ5/fdLWKCw+uAqBG/U4+v2+qdOnSfPPNN4SHh7Ny5UoeP36MiYkJFSpUYMKECVqrzhUoUIAVK1Ywe/ZsQkND2bhxI7a2ttSvX1+zspuZmRnLli3jp59+YuPGjcTHx1O6dGmt4HOmnL6PW7RoQUBAANu2bWPLli2o1erXBpJ2797Nxo0bNY8vXLigWQWvSJEiWoEklUrFtm3bqFy5MmXK6K7m9KLDhw9z+HDGe0CpVGJvb0/79u0ZMmRIjj4zUVFRzJw5U6ss83GdOnU0gSTImCJnbGzMggULmDp1KpaWlnTs2JFhw4bpBM1TUlI033Uve5uf508++YQ1a9awcuVKYmNjsbe3x8vLi0GDBmm+x+3t7Rk/fjzz58/n22+/JT09naVLl742iDNgwABiYmLYtWsXO3bsoHHjxvz+++9aNzcAHBwcNN9zISEhxMfH4+DgQOPGjTX5l/TtgxBCiHdHoZb1FA0q887p3r1733FPhBAAN57c5ts9U98o75ECBV836keNYrrLFwsh3p6ktGS+3DmB+wmP3qidNpVa0sW1jWE6JYQQQuRBch0q/k16jUhSq9WsXr2adevWcfv27SxXp1AoFJq7S0II8W9Tq9Wkq1XMPbH0jZNnq1Ez/88V/OI5lgImH95IECHyquWnN7xxEAlgy6VQajtWpbxtaQP0SgghhBDiv02vQNLUqVP5448/qFSpEp988olm9QYhhMgrFAoFB64f4+bTKIO09+T5M0Iu7zFIAm4hxOvdibtP6FXdnEn6UKlVLPt7A9+7jzBIe0IIIYQQ/2V6BZI2bdpEixYtdOaRCyFEXrLrarhB29t77TB+lb1RKpSyXLEQb9nuqwdQY7jZ95ceXeXW02hKWDsarE0hhBBCiP8ivZYzSUpKeivLlAohhKE8THjM9Se3Ddrms6RYLj+6JkEkIf4FJ6L+Nnibx99Cm0IIIYQQ/zV6BZLq16/P2bNnDd0XIYQwmGsxt95Ou0/eTrtCiAxqtZrY5HgeJcYYvG35/AohhBBCvDm9Aknfffcdp0+fZt68eTx58sTQfRJCiDd2N/7B22k37v5baVcIkUGhUHAvTj6/QgghhBB5lV45kjw9PVGr1cycOZOZM2diamqKkZF2TEqhUPDXX38ZpJNCCJFb6ar096pdIcT/pMnnVwghhBAiz9IrkNSyZUvJESKEyNPMTfK/nXaNzd9Ku0KI/ynwtj6/JvL5FUIIIYR4U3oFkiZPnmzofgghhEGVsnZ6K+2WLvR22hVCZFCr1ThaFsXYKB+pqjSDti2fXyGEEEKIN6dXjiQhhMjL1Go1pQs5Yaw0NnjbFWxLG7xNIcT/KBQK8hkpKWNT0uBtO9uWMXibQgghhBD/NTkakbRp0yYAPv30UxQKhebx67Rp00bPbgkhhP4UCgVm+Uxp4FST8BvHDNZuJfvyFLEobLD2hBDZa1a6AZcfRRqsvfz5zKhfoiZqtVqm5wshhBBCvIEcBZICAwNRKBR4e3tjYmJCYGDga/dRKBQSSBJCvFOtKjTjwM3jqNVqg7TX2tnDIO0IIV5NrVbjVrIWwee28OT5M4O06V7WDbN8pgZpSwghhBDivyxHgaS9e/cCYGJiovVYCCHyslKFnPCp4E7I5T1v3Fa94jWo5egqoxmE+BcoFApMlCZ8UcufKQd/feP2Chewo6NLawP0TAghhBBC5CiQ5Ojo+MrHQgiRF6nVajpV+YRLjyK58vi63u04FLSnV61OABJEEuJfolarqVmsCl7lP2bHlf16t2OiNGZwvR6YymgkIYQQQgiD0CvZtru7+ytHJe3fvx93d3e9OyWEEIagUCgwVhrzTeOBONuV1auNYhYOfNd0KJamFgbunRDiVRQKBWq1mh41OtCyXBO92sifz4zARgOoYCdJtoUQQgghDEWvQFJ0dDSJiYnZbk9MTOTOnTt6d0oIIQypgIk54z4eRgcXH5RGyhzto0BBy3JNmNJiFHYFbN5yD4UQWckMJvWs2YlhDXrlKqBbxaEiP3mOxsXB+S32UAghhBDivydHU9uy8qrpHWfPnsXS0lLfpoUQwuCMFEb4VW5F41L1CL16gPAbx3iaFKtTr4CxOQ1K1KJl+SaUsCpmsETdQgj9ZAaT6jvVpGqRjwi7fpS91w5z+5nuDStjo3xULfIRLcs3oWqRj95Bb4UQQgghPnwKdQ6vkpYsWcLSpUsBuHPnDoUKFSJ//vw69eLj44mNjcXHx4dp06YZtrfvgcwpfZKQXIi871FiDLee3iEpLQkTpQnFLYtQxKLwu+6WECIbLya7j09O4MbTKOKS4zFSGOFQ0I7iVsXI9/+jDiUxvhBCiP8SuQ4V/6Ycj0iytbWlfPnyQMbUNgcHBxwcHHTqmZubU7lyZbp06WK4XgohxFtgZ26DnblMWxPiffFiYKiAibnOtLUX741JEEkIIYQQ4u3IcSDJx8cHHx8fAAICAujfvz/169d/ax0TQgghhMhOVoEiCR4JIYQQQrx9euVIWrZsmaH7IYQQQgghhBBCCCHyOL2TbQOkpqZy7do14uLiskxIW7t27TdpXgghhBBCCCGEEELkIXoFklQqFT///DMrV64kKSkp23oXL17Uu2NCCCGEEEIIIYQQIm/RK5A0b948goKC6NixIzVr1uSrr75i5MiRWFpasnLlShQKBV9++aWh+yqEEEIIIYQQQggh3iEjfXbauHEjXl5ejB8/nkaNGgFQuXJlOnTowJo1a1AoFBw7dsygHRVCCCGEEEIIIYQQ75ZegaR79+5Rr149AExMTABISUnRPP7kk0/YvHmzgboohBBCCCGEEEIIIfICvQJJ1tbWJCYmAlCgQAEKFizI7du3terExsa+ee+EEEIIIYQQQgghRJ6hV46kjz76iLNnz2oe161blyVLllCpUiXUajVLly7F2dnZYJ0UQgghhBBCCCGEEO+eXiOSOnToQEpKimY627Bhw4iNjaVr16507dqVhIQEAgMDDdpRIYQQQgghhBBCCPFu6TUiyd3dHXd3d83jcuXKsWfPHo4fP45SqaR69epYW1sbqo9CCCGEEEIIIYQQIg/QK5CUFQsLCzw8PDSPT548Se3atQ3VvBBCCCGEEEIIIYR4x/Sa2vYqe/fupVOnTnTr1s3QTQshhBBCCCGEEEKIdyhXI5IOHz7M0qVLuXXrFlZWVnh6evLZZ58BsGfPHmbMmEFkZCTW1tYMGDDgbfRXCCGEEEIIIYQQQrwjOQ4khYeH07dvX9RqNYUKFeLWrVucPn2ax48f8/z5c5YvX06JEiUYO3Ysvr6+mJqavs1+CyGEEEIIIYQQQoh/WY4DSb///juFCxdm0aJFlC1blri4OIYNG8Yff/yBQqFgzJgxdOrUCaVS+Tb7K4QQQgghhBBCCCHekRznSLpw4QKdO3embNmyQEZy7aFDh5KamkqfPn3w9/eXIJIQQgghhBBCCCHEByzHgaSEhASKFSumVZb5uEqVKobtlRBCCCGEEEIIIYTIc3K1aptCocjysbGxseF6JIQQQgghhBBCCCHypFyt2rZp0yZOnz6teZycnIxCoWDFihXs3btXp/7o0aPfvIdCCCGEEEIIIYQQIk/IVSDp8OHDHD58WKd8z549OmUKhUICSUIIIYQQQgghhBAfkBwHki5duvQ2+wHAzZs3CQoK4vTp01y5coUyZcqwdetWzfb4+HgWL15MeHg4N27cwMTEBFdXV4YNG4azs7OmXlRUFO7u7jrtV61alTVr1miVnTp1iilTpnDx4kVsbW3p3LkzvXv31pnGJ4QQQgghhBBCCPFfl6sRSW/blStXCA8Pp2rVqqhUKtRqtdb2O3fusHr1atq1a8fQoUNJTk5m0aJFdOzYkfXr12tWlMs0fPhw6tatq3lcoEABre03b96kZ8+euLm5MXToUC5fvsxPP/2EUqmkZ8+eb+9AhRBCCCGEEEIIId5DeSqQ1KxZMzw8PAAIDAzk3LlzWtuLFy9OaGgo+fPn15TVq1ePZs2asXLlSsaMGaNVv2TJklSrVi3b5wsKCqJQoUL88ssvmJiYUL9+fWJiYpg3bx4BAQGYmJgY7uCEEEIIIYQQQggh3nO5WrXtbTMyenV3zM3NtYJIkDHKqESJEjx48CDXz3fgwAHc3d21Akbe3t7ExsYSERGR6/aEEEIIIYQQQgghPmR5KpCkj9jYWE0+pZeNGzeOSpUqUb9+fUaPHs3Tp0812xITE7l7967OfmXKlEGhUHDt2rW33XUhhBBCCCGEEEKI98p7H0iaNm0aCoWCzp07a8pMTEzo3LkzEyZMYMmSJXz++efs2LGDzz77jNTUVADi4uIAsLS01GrPxMSE/Pnz8+zZs3/vIIQQ4g0FBgZSvXp1vfePiorC2dmZDRs2GLBXQgghhBBCiA9NnsqRlFvr169nzZo1TJ48mSJFimjKCxcuzLhx4zSP69SpQ/ny5enTpw+hoaF4e3u/g94KIT4Ed+/eZf369YSFhXHz5k2MjIyoUKEC/fr1o0GDBjlqY/fu3Wzfvp2zZ8/y6NEjihQpwscff0z//v21gtvHjx+nW7du2bYzdOhQ+vXr98bHlJ3w8HDOnDnDoEGD3tpzCCGEEEIIId4vbxxISkhI4N69ewAUKVJEZ2W0tyU8PJyxY8fSv39/2rZt+9r6TZo0wdzcnPPnz+Pt7Y2FhQXwv5FJmVJSUnj+/DlWVlZvpd9CiPfb3r17WbhwIR4eHrRt25a0tDQ2b95Mjx49mDhxIu3atXttG2PGjKFw4cJ88sknFCtWjMuXL7N8+XLCw8PZuHEjZmZmAJQtW5apU6fq7L9lyxYOHTqEm5ubwY7L0dGRM2fOkC/f/34WwsPDWbFihQSShBBCCCGEEBp6B5LOnDnDtGnTOHXqFCqVCshIll2zZk2+/PJLqlSpYrBOvuzvv/9myJAhtGnThiFDhujVhrm5OUWLFtXJhXT9+nXUanWWOZeEEKJu3brs378fGxsbTVnnzp359NNPmTVrVo4CSbNmzaJu3bpaZS4uLnz99deEhITQvn17AOzs7Pj000919p87dy6lSpXC1dX1DY/mfxQKBaampgZrTwghhBBCCPFh0itH0unTp+natSsXLlzAz8+PUaNGMWrUKPz8/Lhw4QJdu3blzJkzhu4rAFevXqVPnz7Uq1eP8ePH53i//fv3k5iYqBXgaty4MXv37tXkTQLYvn07lpaWb5RrRIgPzfHjx/H19aVKlSp4eHgQHBzM7NmzcXZ21qq3efNmfH19cXV1pU6dOgwbNoy7d+9q1QkICMDHx4dLly7RtWtXqlatSvPmzdm5cycAJ06coH379ri6utKyZUuOHDmitX/m816/fp2RI0dSs2ZN6tWrx4wZM1Cr1dy9e5d+/fpRo0YN3NzcWLRokdb+KSkpzJw5E19fX2rWrEm1atXo0qULx44dy9FrUb58ea0gEmTkVmvSpAn37t0jPj7+tW28HEQC8PDwACAyMvKV+545c4abN2/SunXrLLffvn2bnj17Uq1aNRo2bMicOXNQq9Wv7dPLOZICAwNZsWIFAM7Ozpr/hBBCCCGEEP9teo1Imj59Og4ODqxcuRJ7e3utbYMGDaJz585Mnz6dxYsX56rd58+fEx4eDkB0dDTx8fGai8s6deqgVqvp2bMnpqamdO/enXPnzmn2LViwIOXKlQNg8uTJKBQKqlWrhqWlJWfOnGH+/Pm4uLhoLtYAevbsSUhICCNGjKBz5878888/BAUFMWzYMExMTPR5aYT44Fy4cIFevXphb2/PoEGDUKlUzJ07VyeY8ttvvzFz5ky8vLzw8/MjJiaG5cuX4+/vz6ZNm7Ry/zx79oy+ffvi7e2Np6cnq1atYvjw4ahUKiZOnEinTp3w8fEhKCiIwYMHExYWRsGCBbWeb9iwYZQtW5YRI0YQHh7Ob7/9hrW1NcHBwdSrV4+RI0cSEhLClClTqFKlCrVr1wYgPj6etWvX4uPjQ/v27UlISGDdunX06tWLtWvXUqlSJb1ep4cPH5I/f37y58+v1/6PHj0CoFChQq+st2XLFoAsA0np6en06tWLqlWr8uWXX3Lw4EFmz55Nenp6rkdvduzYkQcPHnD48OEsp9cJIYQQQggh/pv0CiSdPn2aAQMG6ASRIGMqRocOHfj1119z3e7jx491LnYyHy9duhRAk4/ps88+06pXp04dli1bBmTkFVm1ahVr1qwhKSkJBwcH/Pz8GDx4sFb+j5IlSxIUFMTkyZP54osvsLGxYfDgwXz++ee57rsQH6pZs2ahVCpZtWoVDg4OAHh5eWklrY+Ojmb27NkMHTqUvn37aspbtGhB27ZtWblypVb5gwcP+Pnnn/Hx8QGgQYMGeHl5MWLECIKDg6latSqQ8Vnu2bMnu3fvxtfXV6tfrq6ufP/990BG0KNZs2ZMnjyZ4cOH88UXXwDg4+NDo0aNWL9+vSaQZGVlxb59+7SCxR06dMDLy4tly5YxceLEXL9GN2/eJDQ0FE9PT5RKZa73B1i4cCFKpZKWLVtmWyc9PZ0dO3bg6upKyZIldbYnJyfTqFEjRo8eDUCXLl3o27cvCxcuJCAgQCf49yrVq1enVKlSHD58OMvpdUIIIYQQQoj/Jr0CSUZGRqSnp2e7XaVSYWSU+1lzxYsX5/Lly6+s87rtAO3bt9fkGHmdGjVqsGbNmhzVFeK/Jj09naNHj+Lh4aEJIkFGELZRo0bs378fgNDQUFQqFV5eXsTExGjq2dnZUbJkSY4fP64VSDI3N6dVq1aax2XKlMHS0hIHBwdNEAnQ/Pv27ds6ffPz89P8W6lU4uLiwr1797TKLS0tKV26tNb+SqVSE+xRqVTExsaiUqlwcXHhwoULuX6Nnj9/zpAhQzAzM2PEiBG53h8gJCREMyqqVKlS2dY7evQojx49ok+fPtnW8ff31/xboVDg7+9PWFgYR48e1XrNhRBCCCGEEEIfegWSqlevzooVK/Dx8cHR0VFr2507d1i5ciU1atQwSAeFEO/O48ePSUpKynL0y4tlN27cQK1W06JFiyzbeXEkIGSs8KhQKLTKLCwsKFKkiE4ZQGxsrE6bxYoV06lramqqM+rGwsKCp0+fapVt3LiRRYsWcf36da0cacWLF9f8OyYmRitgbm5urrMqZXp6OsOGDePq1assXLhQK9iWlJSksypkVqM4//zzT7799lsaNmzIsGHDdLa/KCQkBKVSqTUa7EVGRkY4OTlplZUuXRrIGDWW0+MSQgghhBBCiOzoFUgaPnw4/v7+eHl50bx5c80d9OvXr7N3716USqXed+aFEO8flUqFQqHQTM8CUKvVmmCRubm5Vv3spn9lV55VsuisRj3mZP/NmzcTGBiIh4cHPXv2xNbWFqVSyfz587VGLvn5+WmCLwADBw5k0KBBWu2OHj2asLAwfvrpJ+rXr6+1bfv27YwaNUqr7OURlZcuXaJfv36UL1+eWbNm6QTcXpSUlERoaCj169fHzs4u23qvk5PjEkIIIYQQQojs6BVI+uijj1i7di3Tp09n3759PH/+HID8+fPTqFEjhg4dqkl8LYR4f9na2mJqasrNmzd1tr1YVqJECdRqNcWLF9eMgFGr0lElJQJglD/vjHjZtWsXTk5OzJkzR2tU1KxZs7TqTZs2jeTkZM3jl0f6TJkyhQ0bNvDNN99ocj29qGHDhq9ccODWrVv06tULGxsbFi5c+NpRQfv27SMhISHb1dogI6B3+/Ztzd8AMgL8gGb06OuO60UvjxoTQgghhBBCCL0CSQDlypVj7ty5qFQqTU4UGxsbvXIjCSHyJqVSSYMGDdi7dy/379/XTN26efMmBw8e1NRr0aIFv/zyC7N/+Ylv2tQl+dZ5Uu7fRJ2eilqtJl5tjH3JCuQvWw11ehqgPWLp3z6ml5//9OnT/P3331rT5WrWrJltG7///juLFi2ib9++dO/ePcs6hQsXpnDhwllue/jwIZ9//jkKhYKgoKAcJcEOCQkhf/78NG/e/JX1VqxYoUm2rVarWbFiBcbGxpoRU686rpdlrkAXGxurteqeEEIIIYQQ4r9L70BSJiMjozeaZiGEyNsGDhzIoUOH6Ny5M507d0alUrF8+XLKly/PxYsXAXC0s6Zn81os2LmH6xFHqO9kQX5jJffiUzh6KxbPCjb4GaWRdPsCSVHXSTPKjzotBYWx6b9+PE2bNmX37t0MGDCApk2bEhUVRXBwMOXKlSMxMfG1+4eGhjJt2jRKlSpFmTJl2Lx5s9Z2Nze3134n9urVi9u3b9OrVy/++usv/vrrL802Ozs73NzctOo/ffqUgwcP0qJFi1eOXDI1NeXgwYN8/fXXuLq6cvDgQcLCwujbt2+uVmzLVLlyZQAmTJhAw4YNUSqVkrBbCCGEEEKI/7gcBZLmzJmT64YVCgUDBgzI9X5CiLzFxcWFhQsXMnXqVGbOnEnRokUZPHgw165d49q1azy/cY77G3+mbeEECjdxYuPFx6w48xAA+wLG1ChWkHpOFi+0qEb1PI6ohSMo0iEQE7viWT/xW+Lr68ujR49YvXo1hw4doly5ckybNo2dO3dy4sSJ1+5/6dIlICPB+FdffaWzfenSpa8NJGW28fvvv+tsq1Onjk4gaefOnaSmpmY5he5FSqWS33//nXHjxjFt2jQKFCjAwIED9f4ubtGiBQEBAWzbto0tW7agVqslkCSEEEIIIcR/nEKdVRbbl1SsWDH3DSsUmtEK/yXu7u4A7N279x33RIi3q3///vxz8TwLPYuhTkvRqw0jc0uKBUzAxM7x9ZWFEEIIIYQQWZLrUPFvytGIpMy750KI/6akpCTMzMw0j2/cuEF4eDge5Wz0DiIBqBJjub9+GsV7TgNlPknuLIQQQgghhBB53BvnSBJCfPg8PDxo27YtTk5OREdHExwcjLER+FW0euO2Ux/d5smhtdg07WKAngohhBBCCCGEeJtyHEg6c+YMJUqUwNra+rV1b9++zV9//UWbNm3eoGtCiLyiUaNGbNu2jYcPH2JiYkLVypXoZBeDo6VhkmU/O7kN6/ptUJjkl1FJQgghhBBCCJGHGeW0YseOHbWW+3769ClVq1bNMjltREQEo0aNMkwPhRDv3KRJk9i3bx9nz57lr7/+YmrXZpSzzW+w9tUpScSdOyBBJCGEEEIIIYTI43IcSHo5J7darSY5OZn09HSDd0oIkbc9v3HW4G0mXTd8m0IIIYQQQgghDEtyJAkhckytVqNOSyX1UZTB206+F2nwNoUQQgghhBBCGFaORyQJIYRCoSA98RmoVQZvOy3+icHbFEIIIYQQQghhWBJIEkIIIYQQQgghhBA5kqupbdHR0Zw/fx6AuLg4AG7evImlpaVWvagow097EUK8e2q1GmUBKzBSgsqw+dHyWdgatD0hhBBCCCGEEIaXq0DSzJkzmTlzplbZ+PHjdeqp1WpZfUmID5BCoUCRzwQTeydS7t8waNumRcsatD0hhBBCCCGEEIaX40DSpEmT3mY/hBDvkfylqxo8kJS/tKtB2xNCCCGEEEIIYXg5DiS1bdv2bfZDCPEesazenGfHtgBqg7SnMDWnYOVGMppRCCGEEEIIIfI4SbYthMg1Y5uiFHRpbLD2rOt9gpGJmQSRhBBCCCGEECKPk0CSEEIvti0+R1nQ+o3bMSlSGusGvqjVhhndJIQQQgghhBDi7ZFAkhBCL8r8BXHw+xqFiZn+bVjY4NDuSxRGShmNJIQQQgghhBDvAQkkCSH0ZuZYgaJdvkNpaZfrfU0Kl6RYwASMrR3eQs+EEEIIIYQQQrwNEkgSQrwRM8cKOH0xHYvqzcHo9fn7FcZmWDdsj+PnUzEuJEEkIYQQQgghhHif5HjVNiGEyI6RqTn23n0p1LgTcaf3knTzHMl3r6FKigdAWdAa06LlyF+mGhYuTTAyM5ecSEIIIYQQQgjxHnqjQFJqairXrl0jLi4uy4vC2rVrv0nzQoj3TL6C1hRyawdu7QBQp6cBoFD+76sm87tCciIJIYQQQgghxPtHr0CSSqXi559/ZuXKlSQlJWVb7+LFi3p3TAjx/nsxgKQpkwCSEEIIIYQQQry39AokzZs3j6CgIDp27EjNmjX56quvGDlyJJaWlqxcuRKFQsGXX35p6L4KIYQQQgghhBBCiHdIr2TbGzduxMvLi/Hjx9OoUSMAKleuTIcOHVizZg0KhYJjx44ZtKNCCCGEEEIIIYQQ4t3SK5B079496tWrB4CJiQkAKSkpmseffPIJmzdvNlAXhRBCCCGEEEIIIUReoFcgydramsTERAAKFChAwYIFuX37tlad2NjYN++dEEIIIYQQQgghhMgz9MqR9NFHH3H27FnN47p167JkyRIqVaqEWq1m6dKlODs7G6yTQgghhBBCCCGEEOLd02tEUocOHUhJSdFMZxs2bBixsbF07dqVrl27kpCQQGBgoEE7KoQQQgghhBBCCCHeLb1GJLm7u+Pu7q55XK5cOfbs2cPx48dRKpVUr14da2trQ/VRCCGEEEIIIYQQQuQBegWSsmJhYYGHh4ehmhNCCCGEEEIIIYQQecwbBZLi4+O5c+cOsbGxqNVqne21a9d+k+aFEEIIIYQQQgghRB6iVyDpyZMn/PDDD+zevZv09HSd7Wq1GoVCwcWLF9+4g0IIIYQQQgghhBAib9ArkDRmzBj2799PQEAAtWrVwtLS0tD9EkIIIYQQQgghhBB5jF6BpMOHD9O9e3e++uorQ/dHCCGEEEIIIYQQQuRRRvrsZGZmhqOjo6H7IoQQQgghhBBCCCHyML0CSZ988gl79uwxdF+EEEIIIYQQQgghRB6m19S2li1bcvLkSXr27EnHjh0pUqQISqVSp17lypXfuINCCCGEEEIIIYQQIm/QK5DUpUsXzb+PHDmis11WbRNCCCGEEEIIIYT48OgVSJo0aZKh+yGEEEIIIYQQQggh8ji9Aklt27Y1dD+EEEIIIYQQQgghRB6nV7LtFz148IBLly6RmJhoiP4IIYQQQgghhBBCiDxK70DSnj178PT0pEmTJrRt25bTp08DEBMTQ5s2bQgNDTVYJ4UQQgghhBBCCCHEu6dXIGnfvn0MGjSIQoUKMWDAANRqtWabjY0NDg4ObNiwwWCdFEIIIYQQQgghhBDvnl6BpLlz51KrVi1WrVqFv7+/zvZq1arJim1CCCGEEEIIIYQQHxi9AklXrlzBy8sr2+12dnY8fvxY704JIYQQQgghhBBCiLxHr0BS/vz5ef78ebbbb9++jbW1tb59EkIIIYQQQgghhBB5kF6BpLp167Jp0ybS0tJ0tj18+JA1a9bQsGHDN+6cEEIIIYQQQgghhMg79AokDRkyhHv37uHn58fq1atRKBQcOnSI6dOn07p1a9RqNQMGDDB0X4UQQgghhBBCCCHEO6RXIKls2bKsWrUKa2trZs6ciVqtJigoiPnz51OhQgVWrlxJ8eLFDd1XIYQQQgghhBBCCPEO5cvtDqmpqURGRmJtbc0ff/zBs2fPuHnzJmq1GicnJ2xsbN5GP4UQQgghhBBCCCHEO5brEUlGRka0a9eO3bt3A2BlZYWrqytVq1aVIJIQQgghhBBCCCHEByzXgSSlUkmxYsVISUl5G/0RQgghhBBCCCGEEHmUXjmSunbtypo1a3j69KmBuyOEEEIIIYQQQggh8qpc50gCUKlUmJiY0Lx5c1q2bImjoyNmZmZadRQKBZ999pkh+iiEEEIIIYQQQggh8gC9AklTpkzR/HvdunVZ1pFAkhBCCCGEEEIIIcSHRa9A0t69ew3dDyGEEEIIIYQQQgiRx+kVSHJ0dHxtnWfPnunTtBBCCCGEEEIIIYTIo/RKtp2dlJQUduzYQf/+/WnYsKEhmxZCCCGEEEIIIYQQ75heI5JepFarOXr0KCEhIYSGhhIfH4+NjQ0+Pj65buvmzZsEBQVx+vRprly5QpkyZdi6datme3x8PIsXLyY8PJwbN25gYmKCq6srw4YNw9nZWautuLg4Jk2axJ49e0hNTaVRo0aMHj2awoULa9U7deoUU6ZM4eLFi9ja2tK5c2d69+6NQqHQ7wURQgghhBBCCCGE+EDpHUg6d+4cISEhbNu2jUePHqFQKPD29qZr165Uq1ZNr0DMlStXCA8Pp2rVqqhUKtRqtdb2O3fusHr1atq1a8fQoUNJTk5m0aJFdOzYkfXr11O2bFlN3aFDh3L16lXGjRuHqakpM2bMoHfv3qxfv558+TIO++bNm/Ts2RM3NzeGDh3K5cuX+emnn1AqlfTs2VPfl0YIIYQQQgghhBDig5SrQNLt27fZsmULISEh3Lx5EwcHB1q3bq0ZFdSyZUuqV6+ud2eaNWuGh4cHAIGBgZw7d05re/HixQkNDSV//vyasnr16tGsWTNWrlzJmDFjAIiIiODQoUMEBQVpptiVLl0ab29vdu/ejbe3NwBBQUEUKlSIX375BRMTE+rXr09MTAzz5s0jICAAExMTvY9FCCGEEEIIIYQQ4kOT4xxJHTt2pEWLFqxYsYL69euzbNkywsLC+Prrr6lcubJhOmP06u6Ym5trBZEAChQoQIkSJXjw4IGm7MCBA1haWuLm5qYpK1OmDJUqVeLAgQNa9dzd3bUCRt7e3sTGxhIREfGmhyOEEEIIIYQQQgjxQcnxiKTTp09TvHhxAgMDadq0qWZ62LsWGxvLlStXaNCggabs2rVrlC5dWmd6XZkyZbh27RoAiYmJ3L17lzJlyujUUSgUXLt2jbp16779AxBCCCGEEEIIIYR4T+R4RNKYMWOwt7dn4MCBuLm5MXbsWI4dO6aTx+jfNm3aNBQKBZ07d9aUxcbGYmFhoVPXysqKZ8+eARnJuAEsLS216piYmJA/f35NPSGEEEIIIYQQQgiRIcfDivz9/fH39+f27duEhISwdetW1qxZg52dHXXr1kWhUPzrK52tX7+eNWvWMHnyZIoUKfKvPrcQQgghhBBCCCHEf02ORyRlcnJyon///mzfvp1169bRqlUrTpw4gVqtZvz48YwZM4b9+/eTnJz8NvqrER4eztixY+nfvz9t27bV2mZpaUl8fLzOPs+ePcPKygpAM2Ipc2RSppSUFJ4/f66pJ4QQQgghhBBCCCEyvFGiIxcXF1xcXPj66685duwYW7ZsYfv27axdu5b8+fO/tYTVf//9N0OGDKFNmzYMGTJEZ3uZMmU4evQoarVaa5TU9evXqVChApCRuLto0aKanEkv1lGr1Tq5k4QQQgghhBBCCCH+63I9IinLRoyMaNCgAZMnT+bIkSP88ssv1KtXzxBN67h69Sp9+vShXr16jB8/Pss6jRs35tmzZxw9elRTdv36dS5cuEDjxo216u3du5fU1FRN2fbt27G0tKR69epvpf9CCCGEEEIIIYQQ7yuDL71mamqKt7c33t7eud73+fPnhIeHAxAdHU18fDw7d+4EoE6dOqjVanr27ImpqSndu3fn3Llzmn0LFixIuXLlAKhevToNGzbkm2++4euvv8bU1JTp06fj7OxMixYtNPv07NmTkJAQRowYQefOnfnnn38ICgpi2LBhmJiYvMnLIIQQQgghhBBCCPHBUajf9bJrL4iKisLd3T3LbUuXLgWgW7duWW6vU6cOy5Yt0zyOi4tj0qRJhIaGkpaWRsOGDRk9ejQODg5a+506dYrJkydz8eJFbGxs8Pf3p3fv3nonDs/s/969e/XaXwghhBBCCCGEyA25DhX/pjwVSPoQyAdYCCGEEEIIIcS/Sa5Dxb/JIDmShBBCCCGEEEIIIcSHTwJJQgghhBBCCCGEECJH9Aokbdq0iaioqGy3R0VFsWnTJn37JIQQQgghhBBCCCHyIL0CSaNGjSIiIiLb7WfOnGHUqFF6d0oIIYQQQgghhBBC5D16BZJel587MTERpVKpV4eEEEIIIYQQQgghRN6UL6cVL126xKVLlzSP//zzT9LT03XqxcbGEhwcTOnSpQ3TQyGEEEIIIYQQQgiRJ+Q4kLRnzx7mzJkDgEKhYPXq1axevTrLupaWlkyZMsUwPRRCCCGEEEIIIYQQeUKOA0kdOnSgadOmqNVq2rdvz+DBg2ncuLFWHYVCQf78+SlRogT58uW4aSGEEEIIIYQQQgjxHshxtKdw4cIULlwYgKVLl1K2bFlsbW3fWseEEEIIIYQQQgghRN6i17ChOnXqaP599epVoqOjAXB0dKRcuXKG6ZkQQgghhBBCCCGEyFP0nn+2Z88eJk+erAkiZSpevDiBgYG4u7u/ceeEEEIIIYQQQgghRN6hVyApPDycwYMHU6xYMYYNG0bZsmUBiIyMZM2aNQwaNIh58+bp5FASQgghhBBCCCGEEO8vhVqtVud2p44dO5KSksKKFSswNzfX2paYmEiXLl0wNTXNdlW3D1nmSKy9e/e+454IIYQQQgghhPgvkOtQ8W8y0meny5cv06ZNG50gEoC5uTlt27bl8uXLb9w5IYQQQgghhBBCCJF36BVIMjU15dmzZ9luf/bsGaampnp3SgghhBBCCCGEEELkPXoFkurWrcvSpUuJiIjQ2Xb69GmWLVtG/fr137hzQgghhBBCCCHEu+bs7Mzs2bPfdTeEyBP0Srb95Zdf0qlTJ7p06YKrqyulS5cG4Pr165w5cwZbW1tGjhxp0I4KIYQQQgghhMibNmzYwKhRowBYsWIFtWrV0tquVqtp2rQp9+7do2nTpsyfPz9X7a9YsYL8+fPj6+trsD4DHD9+nG7dujFz5kw8PT0N2nZeMXHiRE6ePEl0dDTJyckUK1YMb29vPv/8cwoUKJCjNs6dO8fs2bM5d+4ciYmJFC9enPbt2+Pv749SqdTUc3Z21tovf/78FCtWDB8fH3r06EH+/PkNemzi3dArkOTk5MSWLVuYP38+Bw4cYPv27QAUK1aMbt268cUXX2Bra2vQjgohhBBCCCGEyNtMTU3ZunWrTiDpxIkT3Lt3DxMTE73aXbVqFYUKFTJ4IOm/4OzZs9SsWRNfX19MTU25cOECCxYs4MiRI6xYsQIjo1dPVDp37hydOnWiVKlS9O7dGzMzMw4cOMCPP/7IrVu3GD16tFZ9Nzc3Pv30UyBjMa4///yTmTNncunSJWbNmvXWjlP8e/QKJAHY2tryzTff8M033xiyP0IIIYQQQggh3lNNmjRh586djB49mnz5/ne5uXXrVipXrszTp0/fXef+o1atWqVTVqJECaZMmcKZM2eoVq3aK/fPXI19+fLlWFtbA9CpUye6du3Kxo0bdQJJpUqV0gSSADp37kxqaiqhoaEkJydLPuUPgF45kl70+PFjzpw5w5kzZ3j8+LEh+iSEEEIIIYQQ4j3UqlUrnj59yuHDhzVlKSkp7Nq1i9atW+vUV6lU/PHHH7Rq1YoqVarQoEEDxo4dq7W4U7Nmzbhy5QonTpzA2dkZZ2dnAgICAHj69ClTpkyhdevWVK9enRo1atCrVy8uXbr0Vo8zOjqacePG0bJlS1xdXalbty6DBw8mKipKq96GDRtwdnbmzz//ZMKECdSrV49atWoxduxYUlJSiI2N5auvvqJ27drUrl2bqVOnolartdoICgqiU6dO1K1bF1dXV3x9fdm5c+cb9d/R0RGA2NjY19aNj4/H1NQUS0tLrXJ7e3vMzMxy9Hz29vYoFAqtaXDi/aX3iKSjR48ybdo0Ll68qFVeqVIlRo4cSYMGDd64c0KId8fZ2ZmBAwcyaNCgd90VLXm1X0IIIYQQIiNAUa1aNbZt20aTJk0AOHDgAHFxcXh7e7Ns2TKt+mPHjmXjxo34+voSEBBAVFQUK1as4MKFC6xatQpjY2O++eYbfvjhB8zNzenbty8AdnZ2ANy+fZs9e/bg6elJ8eLFefToEatXr6Zr165s27YNBweHt3KcZ8+eJSIiglatWlGkSBGio6NZtWoV3bp1Y9u2bTq5gCZMmICdnR2DBg3i9OnTrF69GgsLCyIiIihatCjDhg3jwIEDBAUFUaFCBdq0aaPZd+nSpTRr1ozWrVuTmprKtm3bGDJkCPPnz6dp06Y56m9aWhqxsbGkpqZy5coVZsyYQYECBXB1dX3tvnXq1GH79u2MHTuWHj16aKa2hYaG8uWXX+rUT05OJiYmBoDnz59z6tQpNm7ciI+Pj9YoNfH+0uuvGBoaypAhQ7C1taVXr16UKlUKyEi2vXnzZnr37s2MGTNo3ry5IfsqhNDD2058+LLw8HDOnDmTJwI9OU0KmBN3795l/fr1hIWFcfPmTYyMjKhQoQL9+vXTCZwfPXqULVu2cOrUKe7du4ednR316tVjyJAhFC5cWKtus2bNiI6O1jw2MTGhaNGiuLu706dPH83w4VcJDAxk165dWa6kaQh54W/6NvsQGRnJxIkTOXXqFMbGxjRp0oRRo0ZhY2Pz2n23b9/Ovn37OHPmDDdv3qROnTo6J8hCCCHEf03r1q35+eefSUpKwszMjJCQEGrXrq0T1Pnzzz9Zu3YtP/30k9Zopbp169KrVy927txJ69at8fDwYMaMGRQqVEhryhRk3GTctWuXVp6fTz/9FC8vL9atW8eAAQPeyjE2bdpUJzn3xx9/TMeOHdm1a5dWIAgyUsMsXLgQhUKBv78/t27dIigoiI4dOzJ+/HgAOnbsSLNmzVi/fr3W/rt27dIa+ePv74+vry+LFy/OcSDp3LlzdOzYUfO4dOnS/Pbbbzk61+zQoQNXr15l9erVrF27FgClUsmYMWPo3LmzTv1169axbt06rTIPDw8mTJiQo76KvE+vQNKMGTMoX748K1asoGDBglrb+vbtS+fOnSWQJEQek9vEh2fOnNFr6Gl4eDgrVqx454Gk3CYFfJ29e/eycOFCPDw8aNu2LWlpaWzevJkePXowceJE2rVrp6k7bdo0nj17hqenJ6VKleL27dssX76csLAwNm3ahL29vVbblSpVokePHkDG0O9z586xdOlSTp48qfMj/C7khb/p2+rDvXv38Pf3x8LCgmHDhpGYmMiiRYv4559/WLt27WsTgq5atYpz585RpUoVyfkghBBC/D8vLy8mTpzI/v37adSoEWFhYVmee+3cuRMLCwvc3Nw0I1gAKleujLm5OcePH89yOtyLXvytTk9PJzY2FnNzc0qXLs2FCxcMd1AveTGwk5qaSnx8PCVKlMDS0pILFy7oBJL8/PxQKBSax66urkRERODn56cpUyqVuLi4cP78+Wyf69mzZ6Snp1OzZk22bduW4/6WK1eOxYsXk5iYSEREBEePHiUxMTFH+yqVSpycnGjYsCGenp6YmJiwbds2JkyYgL29PR4eHlr13d3d6dq1K5AxIun06dP88ccfjBgxglmzZmm9DuL9pFcg6fbt24wYMUIniARQsGBB/Pz8+OWXX964c0IIw8lt4sN/IwleWloaKpVK79U7XiW3SQFfp27duuzfv19rlErnzp359NNPmTVrllYgadSoUdSsWVPrzlijRo3o2rUry5cvZ9iwYVptOzg4aN1da9++Pebm5ixatIgbN25oRn2+D97m3/RtmDdvHs+fP2fDhg0UK1YMyDix69GjBxs3btS6c5eVqVOn4uDggJGRET4+Pv9Gl4UQQog8z8bGhvr167N161aSkpJIT0+nZcuWOvVu3rxJXFwc9evXz7KdnOTgValULF26lJUrVxIVFUV6erpm24ujbR4+fKi1n4WFRY7z+2QlKSmJ+fPns2HDBu7fv6+V1yguLk6nfuZ5xovPD1C0aFGd8hfzQwHs37+f3377jYsXL5KSkqIpfzEgo1KpUKvVmuM0MzPTPAdkXKdnjqL38PAgJCSE/v37s3HjRipWrEhKSorO89rY2KBUKlmwYAFLly5l165dFChQAABvb28CAgIYP348TZs21bq+KFKkiNaIfXd3d6ytrZkyZQr79++nWbNmOq+PeL/olWy7TJkyWhHjlz1+/Pi9uvAR4r8gt4kPnZ2dmT17NpDxQ+np6YmnpydJSUmaOk+fPqVhw4Z06tSJ9PR0AgMDWbFihWb/zP8AoqKicHZ2JigoiD/++AMPDw+qVKlCZGQkKSkpzJw5E19fX2rWrEm1atXo0qULx44d0/t4c5oUcNasWVSsWJGjR49q1RszZgwuLi6aRI3ly5fXmepkYmJCkyZNuHfvHvHx8Zry2rVr6yyjWrt2baytrbl27VqO+p85aknfhITNmjWjT58+/Pnnn/j5+VGlShXc3d3ZtGmTVr3U1FTmzJlDixYtqFKlCnXr1qVz586a94m+f9PMxJIvJ5w8fvw4zs7OHD9+XKv89OnT9O7dm9q1a1OtWjVat27NkiVLXtuHrHz//fdUr16d58+f62wbPnw4bm5umpPM3bt307RpU62TuwYNGlCqVCl27Njx2te5aNGir10yVwghhPgv8vHx4cCBAwQHB9O4cWOdczLICH7Y2tqyePFirf8WLVrE4sWLGTJkyGufZ968eUyaNIlatWoxbdo0goKCWLx4MeXLl9cK7jRs2FDrv+3bt7/R8f3www/MmzcPLy8vZsyYoemztbW1TrJsINvzhdedR/z555/069cPU1NTvvvuOxYsWMDixYvx8fHRep5Hjx5x9+5dzfH9+OOPr2y3RYsWAJpRTRERETqv0d27dwFYuXIldevW1QSRMrm7u/PgwQOtNA3ZyQwWnjx58rV1Rd6n14ikL7/8kuHDh1OlShWdYWyhoaGsXr2a6dOnG6SDQgjDyG3iwxeZmZkxZcoUOnfuzPTp0zU5l77//nvi4uKYNGkSSqWSjh078uDBAw4fPszUqVOzbGvDhg0kJyfToUMHTExMsLKyIj4+nrVr1+Lj40P79u1JSEhg3bp19OrVi7Vr11KpUqVcH29OkwL269eP/fv38+2337JlyxYKFizIwYMHWbNmDUOGDKFixYqvfJ6HDx+SP39+nYSKL0tISCAhIYFChQrpbEtLS9ME51NSUrhw4QKLFy+mdu3aODk55frYM928eZMhQ4bg5+dH27ZtWb9+PYGBgVSuXJny5csDMGfOHObPn0/79u1xdXUlPj6ec+fOcf78edzc3PT+m+bG4cOH6dOnD4ULF6Zbt27Y2dkRGRlJWFgY3bt3z1EfXuTt7c2KFSsICwvDy8tLU/78+XP2799P27ZtUSqV3L9/n8ePH+Pi4qLThqurKwcOHMjVcQghhBDif5o3b853333H33//ne21oaOTI0eOHuGJ9XPSFOkYK/NRzMKBMoVKYGmWMZpGrVajUCiynQ61a9cu6taty8SJE7XKY2Njtc67Fi9erLW9XLlyb3J4mjxIgYGBmrLk5OQsRyO96fOYmpoSFBSkNeJ7/fr1WvWsra1RqVRMmzYNQCcv58tSUlJQqVSa/lasWFHnNcq8sfno0SNUKpVOG6mpqUDGuezrZNbJ6XQ6kbflKJCUmRn/RYUKFWLQoEEULlyYEiVKAHDr1i0ePHhAqVKlWLZsmazcJkQek9PEh1mpWrUqvXr1YuHChTRv3pxHjx6xbds2vvnmG0qXLg1A9erVKVWqFIcPH9ZJhJjp3r17hIaGao3uSU9PZ9++fVo/jh06dMDLy4tly5bpnBjkRE6TAhobGzNlyhR8fX2ZPHkyX331Fd9++y0uLi588cUXr3yOmzdvEhoaiqen52tHDi1ZsoTU1FStwEamQ4cO6QzprlGjhmZEmL6uX7+ulWDdy8uLJk2asGHDBr7++msAwsLCaNKkCT/88EOWbej7N82p9PR0xo4dS+HChdm0aZPW3crMu2w56cOLatasiYODAzt27NB6vcPCwkhMTMTb2xuABw8eAOjkrMose/r0KSkpKe/NND0hhBAiLylQoADjxo0jOjpaM5Up87c9MfU5c48v4e8C11Clq5j8yxSKNi+rtX8l23I0dqyDe6VGAOTPnz/LpeqVSqXOCKAdO3Zw//59SpYsqSkz9LVpVud+y5Yt05paZ6jnUSgUWu1GRUWxd+9erXqZ5ysvH2dsbCz58+fH2NhYqzzz/DjzhpqVlVW2r1Hp0qU5cuQIT5480QTn0tPT2bFjBwUKFNDEA15l//79AK+9SSveDzkKJP3zzz9ZlmfO58wcyqZUKilatCjJycnZ7iOEeHdymvgwOwMHDmT//v18/fXXJCYmUqdOHbp165arPrRo0UIn4KBUKjU/xiqVitjYWFQqFS4uLnonScxNUsAKFSowePBgfv75Zy5fvsyTJ09YtGjRK5cnff78OUOGDMHMzIwRI0a8si8nT55k7ty5eHl5ZZkDoGrVqgwdOhTIuDt06dIlgoKC6NevH3/88Yfe8/fLlSunlVzdxsaG0qVLc/v2bU2ZpaUlV65ceaNcTFn9TXPqwoULREVFMWrUKJ0h7/omYlQoFHh6erJ69WoSEhI0w7B37NiBg4MDNWvWBDLuGgJZBooyc4QlJSVJIEkIIYTQU9u2bbUep6rSiE9O4NLDqyTfKIBZCQtsaxXjwcGbPL8Xj0VZGxRKBcmPEzl//hBHvY9w6OM/6Vs7gMqVK7Nq1Sp+/fVXSpYsqcnD1LRpU+bOncuoUaOoXr06//zzDyEhIbke1b179+4sUxC0bdtWJ48RZKzatnnzZgoWLEi5cuX4+++/OXLkSI5WQcuNJk2asHjxYnr16oWPjw+PHz9m5cqVlChRgsuXL792/xMnTjBhwgRatmxJyZIlSU1N5a+//mL37t24uLjwySefvLaN3r178+WXX1KvXj2aNGlCo0aN2LZtG+fPn2fo0KE6QaobN26wefNmIONc6u+//2bTpk0UKFCA+fPnZ7nSm3i/5CiQtG/fvrfdDyHEvyCniQ+zY2JiwsSJE/Hz88PU1JSJEyfm+mK/ePHiWZZv3LiRRYsWcf36dc0w2VfVBwyaFLBnz55s27aNM2fOMHz48FcOd05PT2fYsGFcvXqVhQsXvnJEV2RkJAMHDqR8+fLZLnlaqFAhrTtATZs2pXTp0gwePJi1a9cSEBBAUlKSzlDprEbSvCirkx4rKyut12zw4MH079+fli1bUqFCBRo2bMinn36aq7tFr/obvU5mUKtChQp67R8TE6N1h87c3JwCBQrg7e3NkiVL2LdvH61btyYhIYHw8HA6duyoec9mBoteTFqZKTPI9CZJOIUQQgjxP3HJ8UwIn0ViWhJm/C/XTvFPKpK/mAWP/7zD3b2RKIwUmFibUahqEQqUsOL8g3/4aveP9OvUhTt37vD777+TkJBAnTp1qF+/Pn379uX58+eEhISwfft2PvroI+bPn8/PP/+cq/5ltwJanTp1sjyn+vbbbzEyMiIkJITk5GRq1KihCfgYUv369fnxxx9ZuHAhEydOpHjx4owcOZLo6GguX77MnDlzCAsLIzo6GoVCQUBAAP369dOcW1aoUIG6deuyd+9eHj58iFqtpkSJEgwYMICePXsSFhbG9u3bOXv2LI8ePaJIkSJ8/PHH9O/fX3OT75NPPtHkQz1x4gRHjhyhdOnSjB8/nk6dOun0+fDhw5p8m0qlEnt7e9q3b09cXBynT5/W1Hv+/Dm///47derUoW7dugZ93cTbpVeOpJMnT1K2bNls70DHxMQQGRlJ7dq136hzQgjD8/HxYcyYMTx69Egn8WFWiQFfdujQISDjQvvmzZu5vtuT1YX55s2bCQwMxMPDg549e2Jra4tSqWT+/Plao2deFhERoTMiau/evRQvXvyVSQEnTZpEdHS01nDn27dvc/PmTSD7UZiZRo8eTVhYGD/99FO2q4wA3L17l549e1KwYEEWLFiQ5UqX2XkxIWFAQADbt2/X5KbK9Lq7UDlJ1F27dm1CQ0PZu3cvhw8fZt26dSxZsoTx48fTvn37HPU1q79pdgHGrObXvwk/Pz+tBI8DBw5k0KBBVKtWDUdHR3bs2EHr1q3Zv38/SUlJmmlt8L/cAS+v4pJZZm1tLaORhBBCiBzy9fXF19dXp1ytVpOmSmNC+GyuP7nNR8N1p0/Z1nLEtpZjtm0/T03it3Mr+X7KGEpaa9/AMjEx4euvv9ZM28/0qvyfL6pbt26ORva8XMfS0pJJkybp1Ht5EEZ2r8ugQYMYNGiQTvnkyZOZPHmyVpmfnx9+fn46dQsVKsS0adPw8PDg9u3bqNVqEhIS6NGjBxMnTqRdu3aUKFGCKVOmZHtcY8aMoXDhwnzyyScUK1aMy5cvs3z5csLDw9m4caPmPC/z2n7QoEH07Nkz2/Ze9VqmpqZqXW88f/6cOXPmMHDgQAkkvWf0CiR169aNqVOnZrnSE8CxY8cYMWIEFy9efKPOCSEML6vEhy9+oV+9+IArlzJyxxwNu0bq41DMC5pQtLgV5HvG3Llz8fX15dKlS4wePZqQkBCtpUX1mY60a9cunJycmDNnjtb+s2bNeuV+hkoKqFKpCAwMpGDBgnTv3p158+bRsmVLzWoWL5oyZQobNmzgm2++eeVy70+ePOHzzz8nJSWFlStXvjbh4cteTkjYsGFDnWM1FGtra9q1a0e7du1ISEiga9euzJ49WxNI0udvmhmgfHkU1curemQGIv/5559X5i7Irg/Tpk3TjB56sT3ImMq5dOlS4uPj2b59uybhfCYHBwdsbGw4d+6cTrtnzpyROfxCCCGEASgUCoLPbuH6k1tv1M7ztCTmHF/CpOaBKBVGek+B/5DUrVuX/fv3Y2Njg7u7OwDBwcF8+umnzJo1i3bt2r22jVmzZukEcVxcXPj6668JCQnJ8Y3FnHh5Cpx4f+m1ZvHrRi2kpKTovWS1EOLtykx8OGjQIE3iQ4VCQUpyGjciH7Py9xOcPHQDgJTkNOLjknlwN46I4zeZNGU8psYWNK7TgQkTJvLo0SOdRNiZq5dllQwxOwVpa0wAAFT1SURBVJnfFy9+t5w+fZq///77lftlJgV88b/M6UovJgXMlF1SwMWLFxMREcH333/PkCFDqF69OuPGjdOspJbp999/Z9GiRfTt25fu3btn26/ExES++OIL7t+/z4IFC/TKPfRyQsLChQvrHKshvPj6AJrX5sXpXvr8TTNf3xeXeE1PT2fNmjVa9SpXrkzx4sVZunSpTvsvvh+y60PNmjW1XpMXA0ne3t6kpKSwceNGDh48mGWi8xYtWhAWFqZZ3hbg6NGj3LhxA09PT01ZamoqkZGRmgTdQgghhMiZW8/usPWfva+vmAM3n0Yxe8N82rVrp1lBPDg4mNmzZ+Ps7KxVd/Pmzfj6+uLq6kqdOnUYNmyY1u89QEBAAD4+Ply6dImuXbtStWpVmjdvzs6dO4GM/EKZK9u2bNmSI0eOaO2f+bzXr19n5MiR1KxZk3r16jFjxgzUajV3796lX79+1KhRAzc3NxYtWqS1f0pKCjNnzsTX15eaNWtSrVo1unTpwrFjx3L0epQvX15nlpCJiQlNmjTh3r17xMfHv7aNrEYCZeYSjYyMzHKfP/74g48//hhXV1e6du2a4/zIgYGBmuuPqKgozQj8OXPm4OzsjLOz8xsvNiP+HTkekXTnzh2tO8nXrl3TukDIFBsbS3BwMMWKFTNMD4UQBvdi4sOkpFQ2r/qbxIQUjI2yX2Xi3JVQnsTewb1eX04eiubGVQs+696Lhb//hqenJ02aNAEyAgMAEyZMoGHDhiiVSlq1avXK/jRt2pTdu3czYMAAmjZtSlRUFMHBwZQrV07vJUIzkwJ26NCBDh06YGZmlmVSwMjISM0PeOYP2+TJk2nTpg3jx49n5syZAISGhjJt2jRKlSpFmTJlNAkEM7m5uWFnZwfAyJEjOXPmDO3atSMyMlLrR7hAgQJaib4B7t+/r2kvNTWVS5cusXr1agoVKkRAQIBex59TrVq1ok6dOlSuXBlra2vOnj3Lrl276Nq1q6aOPn/T8uXLU61aNX755ReePXuGlZUV27dv11ke1sjIiHHjxtGvXz/atGmDr68v9vb2XLt2jatXrxIUFKR3HypXrkzJkiWZPn06KSkpWtPaMvXt25edO3fSrVs3unXrRmJiIkFBQVSoUEHrLt79+/fx9vambdu2WsPNT548qfktjImJITExkV9//RXIGAIuU7yFEEL81+26Epaj9Ak5kXg3jgULZ1PUoSiDBg1CpVIxd+5cnWDKb7/9xsyZM/Hy8sLPz4+YmBiWL1+Ov7+/ziqxz549o2/fvnh7e+Pp6cmqVasYPnw4KpWKiRMn0qlTJ3x8fAgKCmLw4MGEhYXppCsYNmwYZcuWZcSIEYSHh/Pbb79hbW1NcHAw9erVY+TIkYSEhDBlyhSqVKmiOT+Ij49n7dq1+Pj40L59exISEli3bh29evVi7dq1VKpUSa/X6eHDh+TPn19zIy63Hj16BKBZoe1FmzZtIiEhgS5dupCcnMyyZcvo3r07ISEhmnPhnLCxsWHcuHGMGzeO5s2b07x5cwCdgKDIm3IcSNqwYYNm2olCoWDevHnMmzdPp55arUapVDJ+/HiDdlQIYXjJSWks++0Yd6OevbJezLMozl3di3MpN4rYZSShfngvDlMzZypW/IjRo0ezbds2LC0tadGiBQEBAWzbto0tW7agVqtfe8Hv6+vLo0ePWL16NYcOHaJcuXJMmzaNnTt3cuLECb2O7ZNPPqFQoUIsWLCAoKAg4uPjdZICpqen8/XXX1OoUCG++eYbzb6lSpVi+PDh/Pjjj2zfvh1vb28uXboEZKxC8dVXX+k839KlSzU/npl1169fz/r167XqOTo66gSSLl68qGnTyMiIQoUK0aJFC4YMGfLKRN6GEBAQwL59+zh8+DApKSkUK1aMoUOHas191+dvCvDTTz8xduxYFixYgKWlJX5+ftStW5cePXpo1WvUqBFLlixh7ty5LFq0CLVajZOTEx06dHjjPnh5eTFv3jxKliypCUa9qGjRoixfvpzJkyfz888/Y2xsTJMmTQgMDMxRfqRjx44xZ84crbLM4OPAgQMlkCSEEOI/S61Wo1KrOHRTd/CBvu7vuw4KGDd7Ao0rZ4zO9vLy0rpZFB0dzezZsxk6dCh9+/bVlLdo0YK2bduycuVKrfIHDx7w888/a1IWNGjQAC8vL0aMGEFwcDBVq1YFoGzZsvTs2ZPdu3fr5DxydXXl+++/B6Bjx440a9aMyZMnM3z4cL744gsgI09po0aNWL9+veb8wMrKin379mmdc3To0AEvLy+WLVumM/I/J27evEloaCienp56zxJauHAhSqUyy0V5bt26xe7duzXnqI0bN6Z9+/YsXLhQJ5/nq5ibm9OyZUvGjRuHs7Mzn376qV59Fe+GQp3D8HBkZCRXr15FrVYzdOhQAgICtJaVhozpMfnz56dSpUq5ikZ+SDLnpu7da5jhm0K8DWq1GoVCwerFJ7l87v4btWVpbUbfL5tgZiZznoUQQgghxP/cfBrFl7t+NEhbapWasz+GY1XJnlHff0ubSv8LcvTt25f9+/dz+fJl/vjjDyZPnsyuXbu08ngCdO3aFQcHB03eyYCAAM6dO8epU6e0ci7Vrl0bBwcHtm7dqimLi4ujVq1a9O/fnyFDhgAZU9vmzJnD2rVrcXV11dQdMGAAe/bs4ejRo1qjpdq0aUOBAgVYsWKFzvGpVCpiY2NRqVSMGjWK+/fvs2nTphy/Pu7u7qhUKqysrLhz5w4hISF63ZAMCQlh5MiR9OrViy+//FJTHhUVhbv7/7V35/E1Xfv/x98nkYSQhLRiSoJQaTWGGGJKiYQiKDUWddsaSq/SUt9bXHVVB+PtgLaUoMaaaqxZ1VSqLTW0NUcIWkNIIjM5vz/8cq7TE8lOciSG1/PxyOPh7L322p997ETO21prh6lNmzY2T8Tr0qWL4uLiLFMC7ybjqW8ZC5LHxMSoQYMGloel4MFheERSpUqVVKlSJUnS2LFjVbdu3Tw98hlAwTGZTDq8/3yeQyRJiruerE2rftdzXWvYoTIAAAA8LM5eP599I4NuJqTKnJYuF88iiroebbXvzifxnjlzRmazOdOHpkhSoULWH4FLly5ts3C3m5ubSpcubbNNynzNyL8v6+Lm5iYXFxebKXdubm66fv261bYVK1Zo1qxZioyMtDwURpLVZ+2YmBjduvW/JShcXV1tnkxsNpsVExOjy5cva8aMGVYhUnJyss0DUDIeUHOnn3/+Wf/+978VHByswYMH2+yXrN/rDBUqVND69etzdC482HL11LY711cB8GDJGIS4bf1Ru/X560/n1Ci0sh4rWTT7xgAAAHgkJN1Myb5RLiRn0W96erpMJpNlelbGSPwMrq6uVtvuNv3rbtszm9Dj4GD7DCsjx69atUrDhg1Ts2bN1Lt3bz322GNydHTU9OnTde7cOUu7Tp06Wa1XnNkInmvXrik5OVn//e9/LYtYZ1i3bp3NtLNjx45ZvT569Khee+01PfHEE5o8ebJN4GaUkXPhwZe7u0NSSkqKNm7cqN9//13x8fE2j9k2mUy5mtMJ4N4ymUw68ftfuh6TZL9OzdIve6L07HNV7dcnAAAAHmhOjrn+uGmjUFFnmQo5KCUmSU4O1ksqREVFWf7s6+srs9ksb29vVaxYUenJiUr5K1LpSfGSg4OcintKZrP0t1FIBWHjxo3y8fGxrEWcYfLkyVbtJk6cqJSU/4Vndz6lVpLGjx+vxMREeXh4WNZ6ulNwcLBlOl9mzp49qz59+sjT01MzZsywGe10pzvf6wxnzpxRuXLlDJ3rTn8fCYYHR66+s8+fP69//OMfOn/+vNzd3RUfHy8PDw/Fx8fr1q1bKlGihFxdXe1dKwA7OfGH/R9hfuKPvwiSAAAAYFHOvYzd+jI5mOTmV0Jxf1yW283/PY0sKipKO3fulCTLlLaPPvpIH48Zof9rUFppl89JMlv2x6fckodbUblWqqVbyTfsVl9uZIxaunOE1MGDB/Xrr79aTZerXbv2XfuYOXOmZs2aJTc3N5s1oTJ4eXnJy8sr032XL19Wr169ZDKZFBERYTMd7++2bNmiv/76yzJ17tChQzp48KBeeumlbM/1dxlPlctsuiDub7kKkiZMmKAbN25oyZIl8vb2VsOGDfXxxx+rdu3amjt3rhYsWGB5ZDOA+092T2nLjauXE5SSfFPOLo787wIAAMAjzmw2q0Jxbzk6OOpW+q3sDzCgVGhFxc+I0VejpkkvJik9PV3z58/XE088oT/++EMmk0mPJV7QS3W8NeuH/Tp3wlUNfNxUxMlRf95I1Z6zcWpZxVOdni6khKN7lPrXGaWZnXQz7qoKuT9mlxpzIiQkRJs2bdKAAQMUEhKi6Ohoff3116pcubISExOzPX7z5s2aOHGiKlSooNjYWCUkJGjVqlWW/Y0aNcr2IVh9+vTRuXPn1KdPH/3yyy/65ZdfLPsef/xxNWrUyKq9r6+vunXrpm7duik1NVVz585V8eLF1adPnxxevVS4cGFVrlxZ69evV4UKFVS8eHE98cQTqlKlSo77Qv7KVZC0d+9edevWTdWrV7daLMzZ2Vl9+vTRqVOn9OGHH+rLL7+0V50A7Oh6TPb/MOWYWYq9liivMu727xsAAAAPFJPJJGdHJ9UpW10/Rh+wS5+uZd1V9ZX6St99XZ9++qnKlCmjQYMG6dSpUzp9+rSubJypuJ/Xq7O/m8oW9tGKP65qwaHLkqSSRZ1Uq2wx1fexHrVjTk1W9IzBKtV5mIr45u/o+g4dOujKlStavHixdu3apcqVK2vixInasGGD9u3bl+3xR4/eXvP0zJkzlm3/+te/LH+eO3dutkFSRh8zZ8602RcUFGQTJLVv314ODg766quvdPXqVVWvXl3vvPOO4VFIf/f+++/rvffe09ixY5WWlqbXX3+dIOkBYDJntlpYNmrUqKGRI0eqc+fOunnzpqpVq6ZPP/3UsjL+kiVLNH78eKs081ERFhYmSdq6dWsBVwLc3aRRm5SYkGr3fvu91VilyhIkAQAA4LbfLh3Xu9s+tlt/bf2bq2fNDpbXZrNZAwYM0LFD+zWjVblc92tyKqwyL76rwmUr26PMfMfnUOQn2+XlDShTpoz++uv2Y8MLFSqkUqVK6ddff7XsP3nypFxcXOxSIAD7cy3qfG/6LXZv+gUAAMCDx2w262mvKqrvXcsu/ZUo4qFwvxCrbVFRUdq+/XsFeOStb3Nasi6t/ETpaSmZPpkNwP/kampb/fr1tXXrVr3++uuSpOeff15ffvml4uLilJ6ertWrV6tdu3Z2LRSA/ZT29tCVS/ZdXLCYm4vc3AvbtU8AAAA8uDLWzexT5wUdu3JK15Jzv06nyWRS/7o91S78OT3//PPy8fHR+fPntWjRIhWSWZ0Csp7CZcTNaxd1bedSPRb6Yp77Ah5muQqSXn31VR0+fFipqalydnZW//79denSJW3cuFEODg5q06aNhg8fbu9aAdhJeT9PHdl/3q59+lbK+gkPAAAAeDS5u7jp3yGD9N73nyo2OedP6LodIr2owDJPKzg4WN9++60uX74sZ2dnBVTyVQ/vFJVzt8+MmPj9G1Ximc4yFXLmATLAXeRqjSTcHXNTcb8zm81KTbmpj8dsUWqKfZ6gIUk9+9dXxSfy/j9BAAAAeDhdSriqL/bN1W+Xjhs+5jHXEupf90XVKF1VZrPZJtw5P2eEUs4fs2udJdsOlFv1ELv2ea/xORT5KVdrJAF4cJlMJrkUdlKdhhXs1mdZn+Kq+MTjzCcHAADAXXkVfUz/aTpY/wz6h8oX986yrUdhd3Wo2koftRyVaYhkNptlvnVTKRdP2b3OZDsHU8DDJldT2yQpNjZWa9euVXR0tGJjY20+QJpMJn344Yd5LhCA/ZnNZjVpUUXHjvypq5cT8tSXYyEHPfdCDUli+C8AAACyZDabFVKxgUIqNtCZa+d04mqkzlyPVmJasgo5OMrbvbQqliivql6VVcjhfx9X//57pslkUsqVc1L6TbvXmPpnpN37BB4muQqSdu7cqUGDBikpKUnFihWTu7vt4775QAncv0wmk5ycHNWxZy199fkepSTn/h/gVh0C5FXazY7VAQAA4GGV8TnRbDarQgkfVSjhk2k7IyPd05Py9h+id+032b4PpQEeNrkKksaPH6+SJUtqypQp8vf3t3dNAPJJ6XIeerFffS2e9ZNuxKfk6FhHRwe16hCgWvV8M52vDgAAANxNdr87Gvrd0uEerdTikOuJO8AjIVffeVFRUerZsychEvAQKOdbXK/9q4kCapUzfEwZbw/1GRysWvUJkQAAAFAwnDzLPFD9Ag+LXEWtFSpUUELCvRlGCCD/FXF1VocegXomrLJ+3hOlk39c0rWriVZtXIs5q7zfY6rdwFd+VUpathMiAQAAoCAUKlZCjm6euhUfY9d+Xcr42bU/4GGTqyDpjTfe0JgxY9SmTRt5e2e92j6AB0fJ0m5q9XyA9LyUlJiq2GtJMpulosWc5V68iKUdo5AAAABwPyhaJUhxv2ywc5/17Nof8LAxFCS9//77Nts8PT0VHh6uhg0bqkyZMnJ0dLRpM3LkyLxXCKBAFC7ipCKuzpnuI0QCAADA/cC9dku7BkmFfarK2cvXbv0BDyNDQdL8+fPvuu/777/PdLvJZCJIAh5ghEUAAAC43zmX9JFbzTDF/7o1752ZHOQZ1jPv/QAPOUNB0tGjR+91HQAAAAAA5NhjzV5WUuQh3Yy9nKd+POq3U+FyVexUFfDwukfPSwQAAAAA4N5zcHFV6RdGyrFo8Vz3UfTpYHk27S6z2Wy/woCHlOEgKSUlRaNGjdK8efOybDd37lz95z//UVpaWp6LAwAAAAAgO86Pe6vsSx/KpZx/zg50cFTx4E7yaveGTCYHlncADDAcJC1evFgrVqxQSEhIlu1CQkL0zTffaOnSpXmtDQAAAAAAQ5xKlFLZl97XY8/2UiGPktm0NqmIX02Ve3msPJt0k0SABBhlaI0kSVq/fr2effZZ+fj4ZNnO19dXLVu21Lfffqvu3bvnuUAAAAAAAIwxyaNua7nXaaWk0weVfO4PpfwVqfTEeMnBQU4lSsuldCW5Vq4lJ88y/zuKkUiAYYaDpOPHj6tt27aG2gYGBmrbtm25LgoAAAAAgJz6XyBkkmulQLlWCizQeoCHkeGpbWlpaXJycjLU1snJSampqbkuCgAAAACA3GKEEXDvGA6SvLy8dOLECUNtT5w4IS8vr1wXBQAAAAAAgPuP4SCpYcOGWrVqla5evZplu6tXr2rVqlVq2LBhjouJiorSqFGj1K5dO1WtWlVt2rSxabNu3ToNHDhQjRs3lr+/vyIiImzaREdHy9/f3+arS5cuNm3379+vrl27qnr16mratKm+/PJLHvkIAAAAAACQCcNrJPXt21erV6/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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pricing_model = \"spot\" # @param [\"3-years-cud\", \"1-year-cud\", \"on-demand\", \"spot\"]\n",
        "# Retrieved as of 08/20/2025\n",
        "model_elo_data = [\n",
        "    {\n",
        "        \"model_name\": \"Qwen/Qwen3-32B\",\n",
        "        \"elo_rating\": 1342,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"google/gemma-2-27b-it\",\n",
        "        \"elo_rating\": 1236,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"google/gemma-2-2b-it\",\n",
        "        \"elo_rating\": 1163,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"google/gemma-3-27b-it\",\n",
        "        \"elo_rating\": 1357,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"google/gemma-3-4b-it\",\n",
        "        \"elo_rating\":1293,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"meta-llama/Llama-3.2-1B-Instruct\",\n",
        "        \"elo_rating\": 1067,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"meta-llama/Llama-3.3-70B-Instruct\",\n",
        "        \"elo_rating\": 1276,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\",\n",
        "        \"elo_rating\": 1292,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"meta-llama/Llama-4-Scout-17B-16E-Instruct\",\n",
        "        \"elo_rating\":1276,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\": \"meta-llama/Meta-Llama-3-8B\",\n",
        "        \"elo_rating\": 1171,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"deepseek-ai/DeepSeek-R1\",\n",
        "        \"elo_rating\":1373,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"mistralai/Mixtral-8x22B-Instruct-v0.1\",\n",
        "        \"elo_rating\":1168,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
        "        \"elo_rating\":1138,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"openai/gpt-oss-120b\",\n",
        "        \"elo_rating\": 1368,\n",
        "    },\n",
        "    {\n",
        "        \"model_name\":\"openai/gpt-oss-20b\",\n",
        "        \"elo_rating\":1315,\n",
        "    }\n",
        "]\n",
        "\n",
        "model_elo_dict = {item[\"model_name\"]: item for item in model_elo_data}\n",
        "\n",
        "model_server_info_json = json.dumps({})\n",
        "profiles_json = !curl \"https://gkerecommender.googleapis.com/v1/profiles:fetch\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
        "  -d '{model_server_info_json}'\n",
        "profiles_str = \"\".join(profiles_json)\n",
        "profiles = json.loads(profiles_str)\n",
        "\n",
        "for profile in profiles[\"profile\"]:\n",
        "    model_name = profile[\"modelServerInfo\"][\"model\"]\n",
        "    if model_name in model_elo_dict:\n",
        "        for stats in profile[\"performanceStats\"]:\n",
        "            if \"cost\" in stats and stats[\"cost\"]:\n",
        "                cost_info = stats[\"cost\"][0]\n",
        "                model_elo_dict[model_name][\"output_token_cost_per_million\"] = convert_to_decimal_cost(cost_info[\"costPerMillionOutputTokens\"])\n",
        "                # You might want to add input token cost as well if needed\n",
        "                # model_elo_dict[model_name][\"input_token_cost_per_million\"] = convert_to_decimal_cost(cost_info[\"costPerMillionInputTokens\"])\n",
        "\n",
        "model_elo_cost_data = list(model_elo_dict.values())\n",
        "\n",
        "sns.set_theme(style=\"white\")\n",
        "\n",
        "elo_cost_df = pd.DataFrame(model_elo_cost_data)\n",
        "\n",
        "elo_cost_df['model provider'] = elo_cost_df['model_name'].apply(lambda x: x.split('/')[0])\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "ax = sns.scatterplot(data=elo_cost_df, x='output_token_cost_per_million', y='elo_rating', hue='model provider', s=300)\n",
        "\n",
        "for i, txt in enumerate(elo_cost_df['model_name']):\n",
        "    label = txt.split('/')[-1]\n",
        "    plt.annotate(label, (elo_cost_df['output_token_cost_per_million'][i] + 0.01, elo_cost_df['elo_rating'][i] + 5))\n",
        "\n",
        "plt.ylabel(\"Chatbot Arena Elo Rating\")\n",
        "plt.xlabel(\"$ / Million Output Tokens\")\n",
        "plt.title(\"Intelligence vs. Pricing\", fontsize=20)\n",
        "\n",
        "plt.xlim(elo_cost_df['output_token_cost_per_million'].max() + 0.1, 0)\n",
        "plt.ylim(elo_cost_df['elo_rating'].min() - 50, elo_cost_df['elo_rating'].max() + 50)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHTjTUfS8jK4"
      },
      "source": [
        "### Analyze a Specific Model\n",
        "In the following steps, we will:\n",
        "1.  Select a model and model server to analyze.\n",
        "2.  Optionally select a model server version. If not specified, this defaults to the latest model server version.\n",
        "3.  Visualize the trade-offs between throughput, latency, and cost across different hardware configurations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "S6cUbOx_SN_i"
      },
      "outputs": [],
      "source": [
        "# This is required\n",
        "model = \"google/gemma-2-27b-it\" # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6H2pIjpUN1z",
        "outputId": "8ebe2a3c-6e74-4176-b11e-0d97f0866203"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Supported model servers:\n",
            "- vllm\n"
          ]
        }
      ],
      "source": [
        "model_servers_json = !curl \"https://gkerecommender.googleapis.com/v1/modelServers:fetch?model={model}\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\"\\\n",
        "  -A \"colab-guide/1.0\"\n",
        "\n",
        "\n",
        "model_servers_str = \"\".join(model_servers_json)\n",
        "model_servers_data = json.loads(model_servers_str)\n",
        "\n",
        "print(\"Supported model servers:\")\n",
        "for model_server in model_servers_data[\"modelServers\"]:\n",
        "  print(\"-\", model_server)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DQMy_NHAJLUl"
      },
      "outputs": [],
      "source": [
        "model_server = \"vllm\" # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TS_zCPnbVtou",
        "outputId": "0928555c-d12c-41c0-92d1-b639a7dc3373"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Supported model server versions:\n",
            "- v0.7.2\n"
          ]
        }
      ],
      "source": [
        "model_server_versions_json = !curl \"https://gkerecommender.googleapis.com/v1/modelServerVersions:fetch?model={model}&modelServer={model_server}\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\"\\\n",
        "  -A \"colab-guide/1.0\"\n",
        "\n",
        "model_server_versions_str = \"\".join(model_server_versions_json)\n",
        "\n",
        "model_server_versions_data = json.loads(model_server_versions_str)\n",
        "print(\"Supported model server versions:\")\n",
        "for model_server_version in model_server_versions_data[\"modelServerVersions\"]:\n",
        "  print(\"-\", model_server_version)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "PUzuUuk7VtHe"
      },
      "outputs": [],
      "source": [
        "# Optional, defaults to latest model server version\n",
        "model_server_version = \"\" # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmAplLefSIOL"
      },
      "source": [
        "## Visualizing Performance and Cost tradeoffs\n",
        "We will create a series of visualizations from the benchmarking data we fetched. These charts are designed to help you answer the most critical questions about serving performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4-D-z7fL0OY"
      },
      "source": [
        "### Preparing The Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bP3EFSV_9heK"
      },
      "source": [
        "#### Select Pricing Model and Performance Goals\n",
        "We recommend defaulting to None in the first pass to understand the performance range that can be achieved for your given model, and then specifying performance requirements to get a more detailed understanding of how they fit your requirements."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "2a5a2a4d"
      },
      "outputs": [],
      "source": [
        "pricing_model = \"spot\" # @param [\"3-years-cud\", \"1-year-cud\", \"on-demand\", \"spot\"]\n",
        "max_ntpot = None # @param\n",
        "max_ttft = None # @param\n",
        "max_cost_per_million_output_tokens = None # @param\n",
        "max_cost_per_million_input_tokens = None # @param"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q5_aWcmY9vsh"
      },
      "source": [
        "### Fetching data from the API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "4thTzspiIvnF"
      },
      "outputs": [],
      "source": [
        "# Retrieve benchmarking data\n",
        "model_server_info_payload = {\n",
        "    \"model_server_info\": {\n",
        "        \"model\": model,\n",
        "        \"modelServer\": model_server,\n",
        "        \"modelServerVersion\": model_server_version\n",
        "    }\n",
        "}\n",
        "model_server_info_json = json.dumps(model_server_info_payload)\n",
        "\n",
        "benchmarking_data_json = !curl \"https://gkerecommender.googleapis.com/v1/benchmarkingData:fetch\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
        "  -d '{model_server_info_json}'\\\n",
        "  -A \"colab-guide/1.0\"\n",
        "\n",
        "benchmarking_data_str = \"\".join(benchmarking_data_json)\n",
        "\n",
        "benchmarking_data = json.loads(benchmarking_data_str)\n",
        "\n",
        "# Retrieving performance metrics at the highest price/perf point given performance goals\n",
        "cost_requirements = {\n",
        "    \"pricing_model\": pricing_model\n",
        "}\n",
        "if max_cost_per_million_output_tokens is not None:\n",
        "  cost_requirements[\"cost_per_million_output_tokens\"] = {\"nanos\": int(max_cost_per_million_output_tokens * 1e9)}\n",
        "if max_cost_per_million_input_tokens is not None:\n",
        "  cost_requirements[\"cost_per_million_input_tokens\"] = {\"nanos\": int(max_cost_per_million_input_tokens * 1e9)}\n",
        "\n",
        "profiles_payload = {\n",
        "    \"model\": model,\n",
        "    \"model_server\": model_server,\n",
        "    \"model_server_version\": model_server_version,\n",
        "    \"performance_requirements\": {\n",
        "        \"target_ntpot_milliseconds\": max_ntpot,\n",
        "        \"target_ttft_milliseconds\": max_ttft,\n",
        "        \"target_cost\": cost_requirements,\n",
        "    }\n",
        "}\n",
        "\n",
        "model_server_info_json = json.dumps(profiles_payload)\n",
        "profiles_json = !curl \"https://gkerecommender.googleapis.com/v1/profiles:fetch\" \\\n",
        "  -H \"Content-Type: application/json\" \\\n",
        "  -H \"X-Goog-User-Project: {project_id}\" \\\n",
        "  -H \"User-Agent: curl/7.92.0 google-colab\" \\\n",
        "  -H \"X-Goog-Api-Client: curl/7.92.0 google-colab\" \\\n",
        "  -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
        "  -d '{model_server_info_json}'\\\n",
        "  -A \"colab-guide/1.0\"\n",
        "\n",
        "profiles_str = \"\".join(profiles_json)\n",
        "\n",
        "profiles = json.loads(profiles_str)\n",
        "\n",
        "flat_data = []\n",
        "\n",
        "for profile in benchmarking_data[\"profile\"]:\n",
        "    instance_type = profile[\"instanceType\"]\n",
        "    accelerator_type = profile[\"acceleratorType\"]\n",
        "    model_name = profile[\"modelServerInfo\"][\"model\"]\n",
        "    model_server_name = profile[\"modelServerInfo\"][\"modelServer\"]\n",
        "    model_server_version = profile[\"modelServerInfo\"][\"modelServerVersion\"]\n",
        "\n",
        "\n",
        "    for stats in profile[\"performanceStats\"]:\n",
        "        row = {\n",
        "            \"model_name\": model_name,\n",
        "            \"accelerator_type\": accelerator_type,\n",
        "            \"instance_type\": instance_type,\n",
        "            \"queries_per_second\": stats[\"queriesPerSecond\"],\n",
        "            \"output_tokens_per_second\": stats[\"outputTokensPerSecond\"],\n",
        "            \"ntpot_milliseconds\": stats[\"ntpotMilliseconds\"],\n",
        "            \"ttft_milliseconds\": stats.get(\"ttftMilliseconds\", None)\n",
        "        }\n",
        "\n",
        "        if \"cost\" in stats and stats[\"cost\"]:\n",
        "            cost_info = stats[\"cost\"][0]\n",
        "            row[\"pricing_model\"] = cost_info[\"pricingModel\"]\n",
        "            row[\"cost_per_million_output_tokens_decimal\"] = convert_to_decimal_cost(cost_info[\"costPerMillionOutputTokens\"])\n",
        "            row[\"cost_per_million_input_tokens_decimal\"] = convert_to_decimal_cost(cost_info[\"costPerMillionInputTokens\"])\n",
        "\n",
        "        flat_data.append(row)\n",
        "\n",
        "df = pd.DataFrame(flat_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kw9h7D0-MY74"
      },
      "source": [
        "### Visualizations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y8k7pkk_Rak"
      },
      "source": [
        "This first chart visualizes the fundamental trade-off in serving LLMs: **Throughput vs. Latency**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        },
        "id": "82ba1050",
        "outputId": "1ee6e7b1-840e-4e71-dbe3-0a2887794c82"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
        "fig.suptitle(f\"Performance Metrics\", fontsize=15, y=1.02)\n",
        "\n",
        "# Plot the first graph (Throughput vs. NTPOT) in the left subplot\n",
        "sns.lineplot(\n",
        "    data=df,\n",
        "    x=\"ntpot_milliseconds\",\n",
        "    y=\"output_tokens_per_second\",\n",
        "    hue=\"instance_type\",\n",
        "    marker='o',\n",
        "    alpha=0.8,\n",
        "    palette=\"tab10\",\n",
        "    markersize=8,\n",
        "    ax=ax\n",
        ")\n",
        "ax.set_title(\"Throughput vs. Latency\", fontsize=14)\n",
        "ax.set_xlabel(\"Latency / NTPOT (ms)\")\n",
        "ax.set_ylabel(\"Throughput (Output Tokens / s)\")\n",
        "ax.grid(True, linestyle='--', alpha=0.6)\n",
        "ax.set_xscale('log')\n",
        "ax.set_yscale('log')\n",
        "ax.xaxis.set_major_formatter(mticker.ScalarFormatter())\n",
        "ax.yaxis.set_major_formatter(mticker.ScalarFormatter())\n",
        "\n",
        "\n",
        "handles, labels = ax.get_legend_handles_labels()\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AD9L5qzF_aG8"
      },
      "source": [
        "You can get the throughput, latency and cost data for benchmarking profiles that meet your performance requirements as a table:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "gWxGlJCED-MZ",
        "outputId": "230c1a2f-20f3-4362-fa14-45b619ba84dd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"key_metrics_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Instance Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"a2-highgpu-2g\",\n          \"a3-highgpu-1g\",\n          \"g2-standard-48\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accelerator Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"nvidia-tesla-a100\",\n          \"nvidia-h100-80gb\",\n          \"nvidia-l4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Throughput\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 812,\n        \"min\": 425,\n        \"max\": 2050,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1243,\n          2050,\n          425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model Server\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"vllm\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model Server Version\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"v0.7.2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TPOT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 586,\n        \"min\": 67,\n        \"max\": 1085,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$/Input Token\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"N/A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"$/Output Token\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"N/A\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "key_metrics_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-5ed6629f-27f9-42eb-8810-26ced603ec8c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Instance Type</th>\n",
              "      <th>Accelerator Type</th>\n",
              "      <th>Throughput</th>\n",
              "      <th>Model Server</th>\n",
              "      <th>Model Server Version</th>\n",
              "      <th>TPOT</th>\n",
              "      <th>$/Input Token</th>\n",
              "      <th>$/Output Token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a2-highgpu-2g</td>\n",
              "      <td>nvidia-tesla-a100</td>\n",
              "      <td>1243</td>\n",
              "      <td>vllm</td>\n",
              "      <td>v0.7.2</td>\n",
              "      <td>70</td>\n",
              "      <td>N/A</td>\n",
              "      <td>N/A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a3-highgpu-1g</td>\n",
              "      <td>nvidia-h100-80gb</td>\n",
              "      <td>2050</td>\n",
              "      <td>vllm</td>\n",
              "      <td>v0.7.2</td>\n",
              "      <td>67</td>\n",
              "      <td>0.055</td>\n",
              "      <td>0.222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>g2-standard-48</td>\n",
              "      <td>nvidia-l4</td>\n",
              "      <td>425</td>\n",
              "      <td>vllm</td>\n",
              "      <td>v0.7.2</td>\n",
              "      <td>1085</td>\n",
              "      <td>0.102</td>\n",
              "      <td>0.409</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ed6629f-27f9-42eb-8810-26ced603ec8c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
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              "    .colab-df-container {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "        document.querySelector('#df-5ed6629f-27f9-42eb-8810-26ced603ec8c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('key_metrics_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('key_metrics_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    Instance Type   Accelerator Type  Throughput Model Server  \\\n",
              "1   a2-highgpu-2g  nvidia-tesla-a100        1243         vllm   \n",
              "0   a3-highgpu-1g   nvidia-h100-80gb        2050         vllm   \n",
              "2  g2-standard-48          nvidia-l4         425         vllm   \n",
              "\n",
              "  Model Server Version  TPOT $/Input Token $/Output Token  \n",
              "1               v0.7.2    70           N/A            N/A  \n",
              "0               v0.7.2    67         0.055          0.222  \n",
              "2               v0.7.2  1085         0.102          0.409  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def get_key_metrics(data, model_name_filter):\n",
        "    \"\"\"Extracts key metrics from the profile data and returns a DataFrame.\"\"\"\n",
        "\n",
        "    flat_data = []\n",
        "    for profile in data[\"profile\"]:\n",
        "        if profile[\"modelServerInfo\"][\"model\"] != model_name_filter:\n",
        "            continue\n",
        "\n",
        "        instance_type = profile[\"instanceType\"]\n",
        "        accelerator_type = profile[\"acceleratorType\"]\n",
        "        model_name = profile[\"modelServerInfo\"][\"model\"]\n",
        "        model_server_name = profile[\"modelServerInfo\"][\"modelServer\"]\n",
        "        model_server_version = profile[\"modelServerInfo\"][\"modelServerVersion\"]\n",
        "\n",
        "        for stats in profile[\"performanceStats\"]:\n",
        "            row = {\n",
        "                \"Instance Type\": instance_type,\n",
        "                \"Accelerator Type\": accelerator_type,\n",
        "                \"Throughput\": stats[\"outputTokensPerSecond\"],\n",
        "                \"Model Server\": model_server_name,\n",
        "                \"Model Server Version\": model_server_version[:10],\n",
        "                \"TPOT\": stats[\"ntpotMilliseconds\"],\n",
        "            }\n",
        "\n",
        "            if \"cost\" in stats and stats[\"cost\"]:\n",
        "                cost_info = stats[\"cost\"][0]\n",
        "                row[\"$/Input Token\"] = round(convert_to_decimal_cost(cost_info.get(\"costPerMillionInputTokens\", {})), 3)\n",
        "                row[\"$/Output Token\"] = round(convert_to_decimal_cost(cost_info.get(\"costPerMillionOutputTokens\", {})), 3)\n",
        "            else:\n",
        "                row[\"$/Input Token\"] = \"N/A\"\n",
        "                row[\"$/Output Token\"] = \"N/A\"\n",
        "\n",
        "            flat_data.append(row)\n",
        "\n",
        "    return pd.DataFrame(flat_data)\n",
        "key_metrics_df = get_key_metrics(profiles, model)\n",
        "key_metrics_df = key_metrics_df.sort_values(by=\"Instance Type\")\n",
        "display(key_metrics_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQDynh1v_iv9"
      },
      "source": [
        "Performance is only half the story. Now let's compare the input and output token cost on different accelerators.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "58073498",
        "outputId": "fcfc04a5-c2cd-4b3a-a1c7-079422523a7e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, axes = plt.subplots(1, 1, figsize=(10, 6))\n",
        "sns.lineplot(\n",
        "    data=df,\n",
        "    x=\"ntpot_milliseconds\",\n",
        "    y=\"cost_per_million_output_tokens_decimal\", # set this to cost_per_million_input_tokens_decimal to see input cost tradeoffs\n",
        "    hue=\"instance_type\",\n",
        "    marker='o',\n",
        "    alpha=0.8,\n",
        "    palette=\"tab10\",\n",
        "    markersize=8,\n",
        "    ax=axes\n",
        ")\n",
        "axes.set_title(\"Output Cost vs. Latency\")\n",
        "axes.set_xlabel(\"Latency / NTPOT (ms)\")\n",
        "axes.set_ylabel(f\"$ / Million Output Tokens {pricing_model}\")\n",
        "axes.set_xscale('log')\n",
        "axes.set_yscale('log')\n",
        "axes.grid(True, linestyle='--', alpha=0.6)\n",
        "axes.xaxis.set_major_formatter(mticker.ScalarFormatter())\n",
        "axes.yaxis.set_major_formatter(mticker.ScalarFormatter())\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "ZRbpxWhfGQ3I",
        "outputId": "3e5e8eca-1dbf-47f0-c737-0de6bf201403"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "colors = sns.color_palette(\"viridis\", 2)\n",
        "\n",
        "filtered_key_metrics_df = key_metrics_df[\n",
        "    (key_metrics_df[\"$/Input Token\"] != \"N/A\") &\n",
        "    (key_metrics_df[\"$/Output Token\"] != \"N/A\")\n",
        "].copy()\n",
        "\n",
        "x = np.arange(len(filtered_key_metrics_df[\"Instance Type\"]))\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(filtered_key_metrics_df[\"Instance Type\"], rotation=0, ha=\"center\")\n",
        "\n",
        "rects1 = ax.bar(x - 0.2, filtered_key_metrics_df[\"$/Input Token\"], width=0.4, label='Input Token Cost', color=colors[0])\n",
        "rects2 = ax.bar(x + 0.2, filtered_key_metrics_df[\"$/Output Token\"], width=0.4, label='Output Token Cost', color=colors[1])\n",
        "\n",
        "ax.bar_label(rects1, padding=3, fmt='$%.3f')\n",
        "ax.bar_label(rects2, padding=3, fmt='$%.3f')\n",
        "\n",
        "ax.set_xlabel(\"Instance Type\")\n",
        "ax.set_ylabel(f\"$ / Million Tokens ({pricing_model})\")\n",
        "ax.set_title(\"Input/Output Token Cost vs. Instance Type\")\n",
        "ax.grid(axis='y')\n",
        "\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBojJ_CUBPba"
      },
      "source": [
        "## Beyond This Notebook\n",
        "You've successfully used the GIQ Recommender API to explore performance and cost across hardware configurations on GKE! You can build your own plots to explore other dimensions of performance using our API.\n",
        "\n",
        "Moving from analysis to production, GIQ can also generate a ready-to-deploy kubernetes manifest that allows you serve the model on your cluster with the exact configuration you've chosen.\n",
        "\n",
        "For more details, check out our [official user guide](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/inference-quickstart).\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
